In [3]:
library(Seurat)
library(Signac)
library(ggplot2)
library(ggrepel)
library(ggridges)
library(cowplot)
library(dplyr)
library(RColorBrewer)
library(S4Vectors)
library(sctransform)
Error in library(Signac): there is no package called ‘Signac’ Traceback: 1. library(Signac)
In [4]:
installed.packages()
| Package | LibPath | Version | Priority | Depends | Imports | LinkingTo | Suggests | Enhances | License | License_is_FOSS | License_restricts_use | OS_type | MD5sum | NeedsCompilation | Built | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| abind | abind | /projects/ps-renlab2/zhw063/miniconda3/envs/seurat5/lib/R/library | 1.4-5 | NA | R (>= 1.5.0) | methods, utils | NA | NA | NA | LGPL (>= 2) | NA | NA | NA | NA | no | 4.3.2 |
| AnnotationDbi | AnnotationDbi | /projects/ps-renlab2/zhw063/miniconda3/envs/seurat5/lib/R/library | 1.64.1 | NA | R (>= 2.7.0), methods, stats4, BiocGenerics (>= 0.29.2), Biobase (>= 1.17.0), IRanges | DBI, RSQLite, S4Vectors (>= 0.9.25), stats, KEGGREST | NA | utils, hgu95av2.db, GO.db, org.Sc.sgd.db, org.At.tair.db, RUnit, TxDb.Hsapiens.UCSC.hg19.knownGene, org.Hs.eg.db, reactome.db, AnnotationForge, graph, EnsDb.Hsapiens.v75, BiocStyle, knitr | NA | Artistic-2.0 | NA | NA | NA | NA | no | 4.3.2 |
| AnnotationFilter | AnnotationFilter | /projects/ps-renlab2/zhw063/miniconda3/envs/seurat5/lib/R/library | 1.26.0 | NA | R (>= 3.4.0) | utils, methods, GenomicRanges, lazyeval | NA | BiocStyle, knitr, testthat, RSQLite, org.Hs.eg.db, rmarkdown | NA | Artistic-2.0 | NA | NA | NA | NA | no | 4.3.2 |
| askpass | askpass | /projects/ps-renlab2/zhw063/miniconda3/envs/seurat5/lib/R/library | 1.2.0 | NA | NA | sys (>= 2.1) | NA | testthat | NA | MIT + file LICENSE | NA | NA | NA | NA | yes | 4.3.1 |
| assertthat | assertthat | /projects/ps-renlab2/zhw063/miniconda3/envs/seurat5/lib/R/library | 0.2.1 | NA | NA | tools | NA | testthat, covr | NA | GPL-3 | NA | NA | NA | NA | no | 4.3.0 |
| backports | backports | /projects/ps-renlab2/zhw063/miniconda3/envs/seurat5/lib/R/library | 1.4.1 | NA | R (>= 3.0.0) | NA | NA | NA | NA | GPL-2 | GPL-3 | NA | NA | NA | NA | yes | 4.3.0 |
| base | base | /projects/ps-renlab2/zhw063/miniconda3/envs/seurat5/lib/R/library | 4.3.2 | base | NA | NA | NA | methods | NA | Part of R 4.3.2 | NA | NA | NA | NA | NA | 4.3.2 |
| base64enc | base64enc | /projects/ps-renlab2/zhw063/miniconda3/envs/seurat5/lib/R/library | 0.1-3 | NA | R (>= 2.9.0) | NA | NA | NA | png | GPL-2 | GPL-3 | NA | NA | NA | NA | yes | 4.3.0 |
| batchelor | batchelor | /projects/ps-renlab2/zhw063/miniconda3/envs/seurat5/lib/R/library | 1.18.0 | NA | SingleCellExperiment | SummarizedExperiment, S4Vectors, BiocGenerics, Rcpp, stats, methods, utils, igraph, BiocNeighbors, BiocSingular, Matrix, DelayedArray, DelayedMatrixStats, BiocParallel, scuttle, ResidualMatrix, ScaledMatrix, beachmat | Rcpp | testthat, BiocStyle, knitr, rmarkdown, scran, scater, bluster, scRNAseq | NA | GPL-3 | NA | NA | NA | NA | yes | 4.3.2 |
| beachmat | beachmat | /projects/ps-renlab2/zhw063/miniconda3/envs/seurat5/lib/R/library | 2.18.0 | NA | NA | methods, DelayedArray (>= 0.27.2), SparseArray, BiocGenerics, Matrix, Rcpp | Rcpp | testthat, BiocStyle, knitr, rmarkdown, rcmdcheck, BiocParallel, HDF5Array | NA | GPL-3 | NA | NA | NA | NA | yes | 4.3.2 |
| beeswarm | beeswarm | /projects/ps-renlab2/zhw063/miniconda3/envs/seurat5/lib/R/library | 0.4.0 | NA | NA | stats, graphics, grDevices, utils | NA | NA | NA | Artistic-2.0 | NA | NA | NA | NA | yes | 4.3.2 |
| BH | BH | /projects/ps-renlab2/zhw063/miniconda3/envs/seurat5/lib/R/library | 1.81.0-1 | NA | NA | NA | NA | NA | NA | BSL-1.0 | NA | NA | NA | NA | no | 4.3.0 |
| Biobase | Biobase | /projects/ps-renlab2/zhw063/miniconda3/envs/seurat5/lib/R/library | 2.62.0 | NA | R (>= 2.10), BiocGenerics (>= 0.27.1), utils | methods | NA | tools, tkWidgets, ALL, RUnit, golubEsets, BiocStyle, knitr | NA | Artistic-2.0 | NA | NA | NA | NA | yes | 4.3.2 |
| BiocFileCache | BiocFileCache | /projects/ps-renlab2/zhw063/miniconda3/envs/seurat5/lib/R/library | 2.10.1 | NA | R (>= 3.4.0), dbplyr (>= 1.0.0) | methods, stats, utils, dplyr, RSQLite, DBI, filelock, curl, httr | NA | testthat, knitr, BiocStyle, rmarkdown, rtracklayer | NA | Artistic-2.0 | NA | NA | NA | NA | no | 4.3.2 |
| BiocGenerics | BiocGenerics | /projects/ps-renlab2/zhw063/miniconda3/envs/seurat5/lib/R/library | 0.48.1 | NA | R (>= 4.0.0), methods, utils, graphics, stats | methods, utils, graphics, stats | NA | Biobase, S4Vectors, IRanges, GenomicRanges, DelayedArray, Biostrings, Rsamtools, AnnotationDbi, affy, affyPLM, DESeq2, flowClust, MSnbase, annotate, RUnit | NA | Artistic-2.0 | NA | NA | NA | NA | no | 4.3.2 |
| BiocIO | BiocIO | /projects/ps-renlab2/zhw063/miniconda3/envs/seurat5/lib/R/library | 1.12.0 | NA | R (>= 4.3.0) | BiocGenerics, S4Vectors, methods, tools | NA | testthat, knitr, rmarkdown, BiocStyle | NA | Artistic-2.0 | NA | NA | NA | NA | no | 4.3.2 |
| BiocManager | BiocManager | /projects/ps-renlab2/zhw063/miniconda3/envs/seurat5/lib/R/library | 1.30.22 | NA | NA | utils | NA | BiocVersion, remotes, rmarkdown, testthat, withr, curl, knitr | NA | Artistic-2.0 | NA | NA | NA | NA | no | 4.3.2 |
| BiocNeighbors | BiocNeighbors | /projects/ps-renlab2/zhw063/miniconda3/envs/seurat5/lib/R/library | 1.20.0 | NA | NA | Rcpp, S4Vectors, BiocParallel, stats, methods, Matrix | Rcpp, RcppHNSW | testthat, BiocStyle, knitr, rmarkdown, FNN, RcppAnnoy, RcppHNSW | NA | GPL-3 | NA | NA | NA | NA | yes | 4.3.2 |
| BiocParallel | BiocParallel | /projects/ps-renlab2/zhw063/miniconda3/envs/seurat5/lib/R/library | 1.36.0 | NA | methods, R (>= 3.5.0) | stats, utils, futile.logger, parallel, snow, codetools | BH, cpp11 | BiocGenerics, tools, foreach, BBmisc, doParallel, GenomicRanges, RNAseqData.HNRNPC.bam.chr14, TxDb.Hsapiens.UCSC.hg19.knownGene, VariantAnnotation, Rsamtools, GenomicAlignments, ShortRead, RUnit, BiocStyle, knitr, batchtools, data.table | Rmpi | GPL-2 | GPL-3 | NA | NA | NA | NA | yes | 4.3.2 |
| BiocSingular | BiocSingular | /projects/ps-renlab2/zhw063/miniconda3/envs/seurat5/lib/R/library | 1.18.0 | NA | NA | BiocGenerics, S4Vectors, Matrix, methods, utils, DelayedArray, BiocParallel, ScaledMatrix, irlba, rsvd, Rcpp, beachmat | Rcpp, beachmat | testthat, BiocStyle, knitr, rmarkdown, ResidualMatrix | NA | GPL-3 | NA | NA | NA | NA | yes | 4.3.2 |
| BiocVersion | BiocVersion | /projects/ps-renlab2/zhw063/miniconda3/envs/seurat5/lib/R/library | 3.18.0 | NA | R (>= 4.3.0) | NA | NA | NA | NA | Artistic-2.0 | NA | NA | NA | NA | no | 4.3.2 |
| biomaRt | biomaRt | /projects/ps-renlab2/zhw063/miniconda3/envs/seurat5/lib/R/library | 2.58.0 | NA | methods | utils, XML (>= 3.99-0.7), AnnotationDbi, progress, stringr, httr, digest, BiocFileCache, rappdirs, xml2 | NA | BiocStyle, knitr, mockery, rmarkdown, testthat, webmockr | NA | Artistic-2.0 | NA | NA | NA | NA | no | 4.3.2 |
| Biostrings | Biostrings | /projects/ps-renlab2/zhw063/miniconda3/envs/seurat5/lib/R/library | 2.70.1 | NA | R (>= 4.0.0), methods, BiocGenerics (>= 0.37.0), S4Vectors (>= 0.27.12), IRanges (>= 2.31.2), XVector (>= 0.37.1), GenomeInfoDb | methods, utils, grDevices, graphics, stats, crayon | S4Vectors, IRanges, XVector | BSgenome (>= 1.13.14), BSgenome.Celegans.UCSC.ce2 (>= 1.3.11), BSgenome.Dmelanogaster.UCSC.dm3 (>= 1.3.11), BSgenome.Hsapiens.UCSC.hg18, drosophila2probe, hgu95av2probe, hgu133aprobe, GenomicFeatures (>= 1.3.14), hgu95av2cdf, affy (>= 1.41.3), affydata (>= 1.11.5), RUnit, BiocStyle, knitr | Rmpi | Artistic-2.0 | NA | NA | NA | NA | yes | 4.3.2 |
| bit | bit | /projects/ps-renlab2/zhw063/miniconda3/envs/seurat5/lib/R/library | 4.0.5 | NA | R (>= 2.9.2) | NA | NA | testthat (>= 0.11.0), roxygen2, knitr, rmarkdown, microbenchmark, bit64 (>= 4.0.0), ff (>= 4.0.0) | NA | GPL-2 | GPL-3 | NA | NA | NA | NA | yes | 4.3.0 |
| bit64 | bit64 | /projects/ps-renlab2/zhw063/miniconda3/envs/seurat5/lib/R/library | 4.0.5 | NA | R (>= 3.0.1), bit (>= 4.0.0), utils, methods, stats | NA | NA | NA | NA | GPL-2 | GPL-3 | NA | NA | NA | NA | yes | 4.3.0 |
| bitops | bitops | /projects/ps-renlab2/zhw063/miniconda3/envs/seurat5/lib/R/library | 1.0-7 | NA | NA | NA | NA | NA | NA | GPL (>= 2) | NA | NA | NA | NA | yes | 4.3.0 |
| blob | blob | /projects/ps-renlab2/zhw063/miniconda3/envs/seurat5/lib/R/library | 1.2.4 | NA | NA | methods, rlang, vctrs (>= 0.2.1) | NA | covr, crayon, pillar (>= 1.2.1), testthat | NA | MIT + file LICENSE | NA | NA | NA | NA | no | 4.3.0 |
| boot | boot | /projects/ps-renlab2/zhw063/miniconda3/envs/seurat5/lib/R/library | 1.3-28.1 | recommended | R (>= 3.0.0), graphics, stats | NA | NA | MASS, survival | NA | Unlimited | NA | NA | NA | NA | no | 4.3.0 |
| BPCells | BPCells | /projects/ps-renlab2/zhw063/miniconda3/envs/seurat5/lib/R/library | 0.1.0 | NA | R (>= 2.10) | methods, grDevices, magrittr, Matrix, Rcpp, rlang, vctrs, stringr, tibble, dplyr, tidyr, ggplot2, scales, patchwork, scattermore, ggrepel, RColorBrewer, hexbin | Rcpp, RcppEigen | IRanges, GenomicRanges, matrixStats | NA | MIT | NA | NA | NA | NA | yes | 4.3.2 |
| broom | broom | /projects/ps-renlab2/zhw063/miniconda3/envs/seurat5/lib/R/library | 1.0.5 | NA | R (>= 3.5) | backports, dplyr (>= 1.0.0), ellipsis, generics (>= 0.0.2), glue, lifecycle, purrr, rlang, stringr, tibble (>= 3.0.0), tidyr (>= 1.0.0) | NA | AER, AUC, bbmle, betareg, biglm, binGroup, boot, btergm (>= 1.10.6), car, carData, caret, cluster, cmprsk, coda, covr, drc, e1071, emmeans, epiR, ergm (>= 3.10.4), fixest (>= 0.9.0), gam (>= 1.15), gee, geepack, ggplot2, glmnet, glmnetUtils, gmm, Hmisc, irlba, interp, joineRML, Kendall, knitr, ks, Lahman, lavaan, leaps, lfe, lm.beta, lme4, lmodel2, lmtest (>= 0.9.38), lsmeans, maps, margins, MASS, mclust, mediation, metafor, mfx, mgcv, mlogit, modeldata, modeltests, muhaz, multcomp, network, nnet, orcutt (>= 2.2), ordinal, plm, poLCA, psych, quantreg, rmarkdown, robust, robustbase, rsample, sandwich, sp, spdep (>= 1.1), spatialreg, speedglm, spelling, survey, survival, systemfit, testthat (>= 2.1.0), tseries, vars, zoo | NA | MIT + file LICENSE | NA | NA | NA | NA | no | 4.3.0 |
| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ |
| tibble | tibble | /projects/ps-renlab2/zhw063/miniconda3/envs/seurat5/lib/R/library | 3.2.1 | NA | R (>= 3.4.0) | fansi (>= 0.4.0), lifecycle (>= 1.0.0), magrittr, methods, pillar (>= 1.8.1), pkgconfig, rlang (>= 1.0.2), utils, vctrs (>= 0.4.2) | NA | bench, bit64, blob, brio, callr, cli, covr, crayon (>= 1.3.4), DiagrammeR, dplyr, evaluate, formattable, ggplot2, here, hms, htmltools, knitr, lubridate, mockr, nycflights13, pkgbuild, pkgload, purrr, rmarkdown, stringi, testthat (>= 3.0.2), tidyr, withr | NA | MIT + file LICENSE | NA | NA | NA | NA | yes | 4.3.0 |
| tidyr | tidyr | /projects/ps-renlab2/zhw063/miniconda3/envs/seurat5/lib/R/library | 1.3.0 | NA | R (>= 3.4.0) | cli (>= 3.4.1), dplyr (>= 1.0.10), glue, lifecycle (>= 1.0.3), magrittr, purrr (>= 1.0.1), rlang (>= 1.0.4), stringr (>= 1.5.0), tibble (>= 2.1.1), tidyselect (>= 1.2.0), utils, vctrs (>= 0.5.2) | cpp11 (>= 0.4.0) | covr, data.table, knitr, readr, repurrrsive (>= 1.1.0), rmarkdown, testthat (>= 3.0.0) | NA | MIT + file LICENSE | NA | NA | NA | NA | yes | 4.3.0 |
| tidyselect | tidyselect | /projects/ps-renlab2/zhw063/miniconda3/envs/seurat5/lib/R/library | 1.2.0 | NA | R (>= 3.4) | cli (>= 3.3.0), glue (>= 1.3.0), lifecycle (>= 1.0.3), rlang (>= 1.0.4), vctrs (>= 0.4.1), withr | NA | covr, crayon, dplyr, knitr, magrittr, rmarkdown, stringr, testthat (>= 3.1.1), tibble (>= 2.1.3) | NA | MIT + file LICENSE | NA | NA | NA | NA | no | 4.3.0 |
| tidyverse | tidyverse | /projects/ps-renlab2/zhw063/miniconda3/envs/seurat5/lib/R/library | 2.0.0 | NA | R (>= 3.3) | broom (>= 1.0.3), conflicted (>= 1.2.0), cli (>= 3.6.0), dbplyr (>= 2.3.0), dplyr (>= 1.1.0), dtplyr (>= 1.2.2), forcats (>= 1.0.0), ggplot2 (>= 3.4.1), googledrive (>= 2.0.0), googlesheets4 (>= 1.0.1), haven (>= 2.5.1), hms (>= 1.1.2), httr (>= 1.4.4), jsonlite (>= 1.8.4), lubridate (>= 1.9.2), magrittr (>= 2.0.3), modelr (>= 0.1.10), pillar (>= 1.8.1), purrr (>= 1.0.1), ragg (>= 1.2.5), readr (>= 2.1.4), readxl (>= 1.4.2), reprex (>= 2.0.2), rlang (>= 1.0.6), rstudioapi (>= 0.14), rvest (>= 1.0.3), stringr (>= 1.5.0), tibble (>= 3.1.8), tidyr (>= 1.3.0), xml2 (>= 1.3.3) | NA | covr (>= 3.6.1), feather (>= 0.3.5), glue (>= 1.6.2), mockr (>= 0.2.0), knitr (>= 1.41), rmarkdown (>= 2.20), testthat (>= 3.1.6) | NA | MIT + file LICENSE | NA | NA | NA | NA | no | 4.3.0 |
| timechange | timechange | /projects/ps-renlab2/zhw063/miniconda3/envs/seurat5/lib/R/library | 0.2.0 | NA | R (>= 3.3) | NA | cpp11 (>= 0.2.7) | testthat (>= 0.7.1.99), knitr | NA | GPL-3 | NA | NA | NA | NA | yes | 4.3.0 |
| timeDate | timeDate | /projects/ps-renlab2/zhw063/miniconda3/envs/seurat5/lib/R/library | 4022.108 | NA | R (>= 3.6.0) | graphics, utils, stats, methods | NA | RUnit | NA | GPL (>= 2) | NA | NA | NA | NA | no | 4.3.0 |
| tinytex | tinytex | /projects/ps-renlab2/zhw063/miniconda3/envs/seurat5/lib/R/library | 0.48 | NA | NA | xfun (>= 0.29) | NA | testit, rstudioapi | NA | MIT + file LICENSE | NA | NA | NA | NA | no | 4.3.1 |
| tools | tools | /projects/ps-renlab2/zhw063/miniconda3/envs/seurat5/lib/R/library | 4.3.2 | base | NA | NA | NA | codetools, methods, xml2, curl, commonmark, knitr, xfun, mathjaxr, V8 | NA | Part of R 4.3.2 | NA | NA | NA | NA | yes | 4.3.2 |
| triebeard | triebeard | /projects/ps-renlab2/zhw063/miniconda3/envs/seurat5/lib/R/library | 0.4.1 | NA | NA | Rcpp | Rcpp | knitr, rmarkdown, testthat | NA | MIT + file LICENSE | NA | NA | NA | NA | yes | 4.3.0 |
| TTR | TTR | /projects/ps-renlab2/zhw063/miniconda3/envs/seurat5/lib/R/library | 0.24.3 | NA | NA | xts (>= 0.10-0), zoo, curl | xts | RUnit | quantmod | GPL (>= 2) | NA | NA | NA | NA | yes | 4.3.0 |
| tzdb | tzdb | /projects/ps-renlab2/zhw063/miniconda3/envs/seurat5/lib/R/library | 0.4.0 | NA | R (>= 3.5.0) | NA | cpp11 (>= 0.4.2) | covr, testthat (>= 3.0.0) | NA | MIT + file LICENSE | NA | NA | NA | NA | yes | 4.3.0 |
| urltools | urltools | /projects/ps-renlab2/zhw063/miniconda3/envs/seurat5/lib/R/library | 1.7.3 | NA | R (>= 2.10) | Rcpp, methods, triebeard | Rcpp | testthat, knitr | NA | MIT + file LICENSE | NA | NA | NA | NA | yes | 4.3.0 |
| utf8 | utf8 | /projects/ps-renlab2/zhw063/miniconda3/envs/seurat5/lib/R/library | 1.2.4 | NA | R (>= 2.10) | NA | NA | cli, covr, knitr, rlang, rmarkdown, testthat (>= 3.0.0), withr | NA | Apache License (== 2.0) | file LICENSE | NA | NA | NA | NA | yes | 4.3.1 |
| utils | utils | /projects/ps-renlab2/zhw063/miniconda3/envs/seurat5/lib/R/library | 4.3.2 | base | NA | NA | NA | methods, xml2, commonmark, knitr | NA | Part of R 4.3.2 | NA | NA | NA | NA | yes | 4.3.2 |
| uuid | uuid | /projects/ps-renlab2/zhw063/miniconda3/envs/seurat5/lib/R/library | 1.1-1 | NA | R (>= 2.9.0) | NA | NA | NA | NA | MIT + file LICENSE | NA | NA | NA | NA | yes | 4.3.1 |
| uwot | uwot | /projects/ps-renlab2/zhw063/miniconda3/envs/seurat5/lib/R/library | 0.1.16 | NA | Matrix | Rcpp, methods, FNN, RcppAnnoy (>= 0.0.17), irlba | Rcpp, RcppProgress, RcppAnnoy, dqrng | testthat, covr, bigstatsr, RSpectra | NA | GPL (>= 3) | NA | NA | NA | NA | yes | 4.3.0 |
| vctrs | vctrs | /projects/ps-renlab2/zhw063/miniconda3/envs/seurat5/lib/R/library | 0.6.4 | NA | R (>= 3.5.0) | cli (>= 3.4.0), glue, lifecycle (>= 1.0.3), rlang (>= 1.1.0) | NA | bit64, covr, crayon, dplyr (>= 0.8.5), generics, knitr, pillar (>= 1.4.4), pkgdown (>= 2.0.1), rmarkdown, testthat (>= 3.0.0), tibble (>= 3.1.3), waldo (>= 0.2.0), withr, xml2, zeallot | NA | MIT + file LICENSE | NA | NA | NA | NA | yes | 4.3.1 |
| vipor | vipor | /projects/ps-renlab2/zhw063/miniconda3/envs/seurat5/lib/R/library | 0.4.5 | NA | R (>= 3.0.0) | stats, graphics | NA | testthat, beeswarm, lattice, ggplot2, beanplot, vioplot, ggbeeswarm, | NA | GPL (>= 2) | NA | NA | NA | NA | no | 4.3.2 |
| viridisLite | viridisLite | /projects/ps-renlab2/zhw063/miniconda3/envs/seurat5/lib/R/library | 0.4.2 | NA | R (>= 2.10) | NA | NA | hexbin (>= 1.27.0), ggplot2 (>= 1.0.1), testthat, covr | NA | MIT + file LICENSE | NA | NA | NA | NA | no | 4.3.0 |
| vroom | vroom | /projects/ps-renlab2/zhw063/miniconda3/envs/seurat5/lib/R/library | 1.6.4 | NA | R (>= 3.6) | bit64, cli (>= 3.2.0), crayon, glue, hms, lifecycle (>= 1.0.3), methods, rlang (>= 0.4.2), stats, tibble (>= 2.0.0), tidyselect, tzdb (>= 0.1.1), vctrs (>= 0.2.0), withr | cpp11 (>= 0.2.0), progress (>= 1.2.1), tzdb (>= 0.1.1) | archive, bench (>= 1.1.0), covr, curl, dplyr, forcats, fs, ggplot2, knitr, patchwork, prettyunits, purrr, rmarkdown, rstudioapi, scales, spelling, testthat (>= 2.1.0), tidyr, utils, waldo, xml2 | NA | MIT + file LICENSE | NA | NA | NA | NA | yes | 4.3.1 |
| withr | withr | /projects/ps-renlab2/zhw063/miniconda3/envs/seurat5/lib/R/library | 2.5.2 | NA | R (>= 3.2.0) | graphics, grDevices, stats | NA | callr, covr, DBI, knitr, lattice, methods, rlang, rmarkdown (>= 2.12), RSQLite, testthat (>= 3.0.0) | NA | MIT + file LICENSE | NA | NA | NA | NA | no | 4.3.1 |
| xfun | xfun | /projects/ps-renlab2/zhw063/miniconda3/envs/seurat5/lib/R/library | 0.41 | NA | NA | stats, tools | NA | testit, parallel, codetools, rstudioapi, tinytex (>= 0.30), mime, markdown (>= 1.5), knitr (>= 1.42), htmltools, remotes, pak, rhub, renv, curl, jsonlite, magick, yaml, rmarkdown | NA | MIT + file LICENSE | NA | NA | NA | NA | yes | 4.3.1 |
| XML | XML | /projects/ps-renlab2/zhw063/miniconda3/envs/seurat5/lib/R/library | 3.99-0.15 | NA | R (>= 4.0.0), methods, utils | NA | NA | bitops, RCurl | NA | BSD_3_clause + file LICENSE | NA | NA | NA | NA | yes | 4.3.2 |
| xml2 | xml2 | /projects/ps-renlab2/zhw063/miniconda3/envs/seurat5/lib/R/library | 1.3.5 | NA | R (>= 3.1.0) | methods | NA | covr, curl, httr, knitr, magrittr, mockery, rmarkdown, testthat (>= 2.1.0) | NA | MIT + file LICENSE | NA | NA | NA | NA | yes | 4.3.0 |
| xtable | xtable | /projects/ps-renlab2/zhw063/miniconda3/envs/seurat5/lib/R/library | 1.8-4 | NA | R (>= 2.10.0) | stats, utils | NA | knitr, plm, zoo, survival | NA | GPL (>= 2) | NA | NA | NA | NA | no | 4.3.0 |
| xts | xts | /projects/ps-renlab2/zhw063/miniconda3/envs/seurat5/lib/R/library | 0.13.1 | NA | R (>= 3.6.0), zoo (>= 1.7-12) | methods | zoo | timeSeries, timeDate, tseries, chron, tinytest | NA | GPL (>= 2) | NA | NA | NA | NA | yes | 4.3.0 |
| XVector | XVector | /projects/ps-renlab2/zhw063/miniconda3/envs/seurat5/lib/R/library | 0.42.0 | NA | R (>= 4.0.0), methods, BiocGenerics (>= 0.37.0), S4Vectors (>= 0.27.12), IRanges (>= 2.23.9) | methods, utils, tools, zlibbioc, BiocGenerics, S4Vectors, IRanges | S4Vectors, IRanges | Biostrings, drosophila2probe, RUnit | NA | Artistic-2.0 | NA | NA | NA | NA | yes | 4.3.2 |
| yaml | yaml | /projects/ps-renlab2/zhw063/miniconda3/envs/seurat5/lib/R/library | 2.3.7 | NA | NA | NA | NA | RUnit | NA | BSD_3_clause + file LICENSE | NA | NA | NA | NA | yes | 4.3.0 |
| zlibbioc | zlibbioc | /projects/ps-renlab2/zhw063/miniconda3/envs/seurat5/lib/R/library | 1.48.0 | NA | NA | NA | NA | BiocStyle | NA | Artistic-2.0 + file LICENSE | NA | NA | NA | NA | yes | 4.3.2 |
| zoo | zoo | /projects/ps-renlab2/zhw063/miniconda3/envs/seurat5/lib/R/library | 1.8-12 | NA | R (>= 3.1.0), stats | utils, graphics, grDevices, lattice (>= 0.20-27) | NA | AER, coda, chron, ggplot2 (>= 3.0.0), mondate, scales, stinepack, strucchange, timeDate, timeSeries, tis, tseries, xts | NA | GPL-2 | GPL-3 | NA | NA | NA | NA | yes | 4.3.0 |
In [9]:
library(Signac)
In [10]:
sessionInfo()
R version 4.3.2 (2023-10-31) Platform: x86_64-conda-linux-gnu (64-bit) Running under: CentOS Linux 7 (Core) Matrix products: default BLAS/LAPACK: /projects/ps-renlab2/zhw063/miniconda3/envs/seurat5/lib/libopenblasp-r0.3.24.so; LAPACK version 3.11.0 locale: [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8 [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8 [7] LC_PAPER=en_US.UTF-8 LC_NAME=C [9] LC_ADDRESS=C LC_TELEPHONE=C [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C time zone: America/Los_Angeles tzcode source: system (glibc) attached base packages: [1] stats graphics grDevices utils datasets methods base other attached packages: [1] Signac_1.12.0 Seurat_5.0.0 SeuratObject_5.0.0 sp_2.1-1 loaded via a namespace (and not attached): [1] RColorBrewer_1.1-3 jsonlite_1.8.7 magrittr_2.0.3 [4] spatstat.utils_3.0-4 zlibbioc_1.48.0 vctrs_0.6.4 [7] ROCR_1.0-11 Rsamtools_2.18.0 spatstat.explore_3.2-5 [10] RCurl_1.98-1.13 base64enc_0.1-3 RcppRoll_0.3.0 [13] htmltools_0.5.7 curl_5.1.0 sctransform_0.4.1 [16] parallelly_1.36.0 KernSmooth_2.23-22 htmlwidgets_1.6.2 [19] ica_1.0-3 plyr_1.8.9 plotly_4.10.3 [22] zoo_1.8-12 uuid_1.1-1 igraph_1.5.1 [25] mime_0.12 lifecycle_1.0.4 pkgconfig_2.0.3 [28] Matrix_1.6-3 R6_2.5.1 fastmap_1.1.1 [31] GenomeInfoDbData_1.2.11 fitdistrplus_1.1-11 future_1.33.0 [34] shiny_1.7.5.1 digest_0.6.33 colorspace_2.1-0 [37] patchwork_1.1.3 S4Vectors_0.40.1 tensor_1.5 [40] RSpectra_0.16-1 irlba_2.3.5.1 GenomicRanges_1.54.1 [43] progressr_0.14.0 fansi_1.0.5 spatstat.sparse_3.0-3 [46] httr_1.4.7 polyclip_1.10-6 abind_1.4-5 [49] compiler_4.3.2 remotes_2.4.2.1 BiocParallel_1.36.0 [52] fastDummies_1.7.3 MASS_7.3-60 tools_4.3.2 [55] lmtest_0.9-40 httpuv_1.6.12 future.apply_1.11.0 [58] goftest_1.2-3 glue_1.6.2 nlme_3.1-163 [61] promises_1.2.1 grid_4.3.2 pbdZMQ_0.3-10 [64] Rtsne_0.16 cluster_2.1.4 reshape2_1.4.4 [67] generics_0.1.3 gtable_0.3.4 spatstat.data_3.0-3 [70] tidyr_1.3.0 data.table_1.14.8 XVector_0.42.0 [73] utf8_1.2.4 BiocGenerics_0.48.1 spatstat.geom_3.2-7 [76] RcppAnnoy_0.0.21 ggrepel_0.9.4 RANN_2.6.1 [79] pillar_1.9.0 stringr_1.5.1 spam_2.10-0 [82] IRdisplay_1.1 RcppHNSW_0.5.0 later_1.3.1 [85] splines_4.3.2 dplyr_1.1.3 lattice_0.22-5 [88] survival_3.5-7 deldir_1.0-9 tidyselect_1.2.0 [91] Biostrings_2.70.1 miniUI_0.1.1.1 pbapply_1.7-2 [94] gridExtra_2.3 IRanges_2.36.0 scattermore_1.2 [97] stats4_4.3.2 matrixStats_1.1.0 stringi_1.8.1 [100] lazyeval_0.2.2 evaluate_0.23 codetools_0.2-19 [103] tibble_3.2.1 cli_3.6.1 uwot_0.1.16 [106] IRkernel_1.3.2 xtable_1.8-4 reticulate_1.34.0 [109] repr_1.1.6 munsell_0.5.0 Rcpp_1.0.11 [112] GenomeInfoDb_1.38.1 globals_0.16.2 spatstat.random_3.2-1 [115] png_0.1-8 parallel_4.3.2 ellipsis_0.3.2 [118] ggplot2_3.4.4 dotCall64_1.1-0 bitops_1.0-7 [121] listenv_0.9.0 viridisLite_0.4.2 scales_1.2.1 [124] ggridges_0.5.4 leiden_0.4.3 purrr_1.0.2 [127] crayon_1.5.2 rlang_1.1.2 fastmatch_1.1-4 [130] cowplot_1.1.1
In [8]:
install.packages("remotes")
In [5]:
remotes::install_github("bnprks/BPCells")
Skipping install of 'BPCells' from a github remote, the SHA1 (75778b4a) has not changed since last install. Use `force = TRUE` to force installation
In [6]:
options(repr.plot.width=16, repr.plot.height=10)
In [7]:
library(ggforce)
library(ggseqlogo)
library(pheatmap)
library(harmony)
library(EnsDb.Mmusculus.v79)
library(tibble)
Error in library(ggforce): there is no package called ‘ggforce’ Traceback: 1. library(ggforce)
In [8]:
setwd('/projects/ps-renlab2/zhw063/99.MouseBrainPairedTag/')
Experiment 1¶
/projects/ps-renlab2/zhw063/99.MouseBrainPairedTag/01.MouseBrainExp1/merge_mtx
In [5]:
brain.data <- Read10X(data.dir = "/projects/ps-renlab2/zhw063/99.MouseBrainPairedTag/01.MouseBrainExp1/merge_mtx/MouseBrainExp1_RNA/")
In [6]:
brain <- CreateSeuratObject(counts = brain.data, project = "mousebrain", min.cells = 0, min.features = 0)
brain[["percent.mt"]]<-PercentageFeatureSet(brain, pattern="^mt-")
VlnPlot(brain, features=c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol=3, pt.size=0.5)
In [7]:
brain
An object of class Seurat 48517 features across 43814 samples within 1 assay Active assay: RNA (48517 features, 0 variable features)
In [8]:
summary(brain@meta.data$nFeature_RNA)
Min. 1st Qu. Median Mean 3rd Qu. Max.
389 919 1403 1647 2128 6630
In [9]:
summary(brain@meta.data$nCount_RNA)
Min. 1st Qu. Median Mean 3rd Qu. Max.
800 1400 2406 3281 4202 19995
In [10]:
summary(brain@meta.data$percent.mt)
Min. 1st Qu. Median Mean 3rd Qu. Max. 0.0000 0.0000 0.0000 0.1750 0.1739 13.1579
In [11]:
plot1 <- FeatureScatter(brain, feature1 = "nCount_RNA", feature2 = "percent.mt")
plot2 <- FeatureScatter(brain, feature1 = "nCount_RNA", feature2 = "nFeature_RNA")
plot1 + plot2
In [12]:
brain.test <- subset(brain, subset = nFeature_RNA > 500 & nFeature_RNA < 6000 & percent.mt < 2 & nCount_RNA < 18000)
In [13]:
brain
An object of class Seurat 48517 features across 43814 samples within 1 assay Active assay: RNA (48517 features, 0 variable features)
In [14]:
brain.test
An object of class Seurat 48517 features across 43122 samples within 1 assay Active assay: RNA (48517 features, 0 variable features)
In [15]:
brain<-brain.test;rm(brain.test)
VlnPlot(brain, features=c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol=3, pt.size=0.5)
In [16]:
summary(brain@meta.data$nFeature_RNA)
Min. 1st Qu. Median Mean 3rd Qu. Max.
501 926 1412 1648 2132 5997
In [17]:
summary(brain@meta.data$nCount_RNA)
Min. 1st Qu. Median Mean 3rd Qu. Max.
800 1411 2426 3268 4217 17983
In [18]:
summary(brain@meta.data$percent.mt)
Min. 1st Qu. Median Mean 3rd Qu. Max. 0.0000 0.0000 0.0000 0.1316 0.1650 1.9980
In [19]:
bc<-matrix(ncol=4, byrow = T, unlist(strsplit(as.character(colnames(brain@assays$RNA@data)),split=":")))
table(bc[,4])
01 02 03 04 05 06 07 08 09 10 11 12 13 14 15 16 2129 2185 1181 1631 1937 2286 960 1217 5062 4611 3437 2328 3886 4370 2902 3000
In [20]:
brainregion <- rep("CPU",dim(bc)[1])
brainregion[bc[,4]=="03"|bc[,4]=="04"|bc[,4]=="11"|bc[,4]=="12"]<-"HYP"
brainregion[bc[,4]=="05"|bc[,4]=="06"|bc[,4]=="13"|bc[,4]=="14"]<-"HCa"
brainregion[bc[,4]=="07"|bc[,4]=="08"|bc[,4]=="15"|bc[,4]=="16"]<-"HCp"
table(brainregion)
brainregion CPU HCa HCp HYP 13987 12479 8079 8577
In [21]:
modality <- rep("H3K27ac",dim(bc)[1])
modality[bc[,4]=="09"|bc[,4]=="10"|bc[,4]=="11"|bc[,4]=="12"|bc[,4]=="13"|bc[,4]=="14"|bc[,4]=="15"|bc[,4]=="16"]<-"H3K27me3"
table(modality)
modality H3K27ac H3K27me3 13526 29596
In [22]:
brain@meta.data<-cbind(brain@meta.data, cbind(brainregion, modality))
oriBarcode<-bc[,4]
table(bc[,1])
sublib <- bc[,1]
brain@meta.data<-cbind(brain@meta.data, cbind(oriBarcode,sublib))
brain.exp1.rna <- brain
rm(brain.data,brain,bc,plot1,plot2,brainregion,modality,oriBarcode,sublib)
A01 A02 A03 A04 A05 A06 A07 A08 A09 A10 A11 A12 A13 A14 A15 A16 2396 1771 1610 2325 2443 2263 2261 1429 1734 1400 1939 1556 1788 1922 1830 1679 A17 A18 A19 A20 A21 A22 A23 1494 1460 1810 2275 2343 1848 1546
In [26]:
saveRDS(brain.exp1.rna, file = "01.MouseBrainExp1/merge_mtx/brain.exp1.rna.object.rds")
Experiment 2¶
/projects/ps-renlab2/zhw063/99.MouseBrainPairedTag/02.MouseBrainExp2/merge_mtx/MouseBrainExp2_RNA
In [29]:
brain.data <- Read10X(data.dir = "/projects/ps-renlab2/zhw063/99.MouseBrainPairedTag/02.MouseBrainExp2/merge_mtx/MouseBrainExp2_RNA")
In [30]:
brain <- CreateSeuratObject(counts = brain.data, project = "mousebrain", min.cells = 0, min.features = 0)
brain[["percent.mt"]]<-PercentageFeatureSet(brain, pattern="^mt-")
VlnPlot(brain, features=c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol=3, pt.size=0.5)
In [32]:
brain
An object of class Seurat 50750 features across 85899 samples within 1 assay Active assay: RNA (50750 features, 0 variable features)
In [33]:
summary(brain@meta.data$nFeature_RNA)
Min. 1st Qu. Median Mean 3rd Qu. Max.
202 738 941 1054 1241 5807
In [34]:
summary(brain@meta.data$nCount_RNA)
Min. 1st Qu. Median Mean 3rd Qu. Max.
700 1090 1474 1778 2098 16284
In [35]:
summary(brain@meta.data$percent.mt)
Min. 1st Qu. Median Mean 3rd Qu. Max. 0.00000 0.00000 0.00000 0.06421 0.00000 14.94058
In [36]:
plot1 <- FeatureScatter(brain, feature1 = "nCount_RNA", feature2 = "percent.mt")
plot2 <- FeatureScatter(brain, feature1 = "nCount_RNA", feature2 = "nFeature_RNA")
plot1 + plot2
In [37]:
brain.test <- subset(brain, subset = nFeature_RNA > 500 & nFeature_RNA < 4000 & percent.mt < 2 & nCount_RNA < 12000)
brain
An object of class Seurat 50750 features across 85899 samples within 1 assay Active assay: RNA (50750 features, 0 variable features)
In [38]:
brain.test
An object of class Seurat 50750 features across 85133 samples within 1 assay Active assay: RNA (50750 features, 0 variable features)
In [39]:
brain<-brain.test;rm(brain.test)
VlnPlot(brain, features=c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol=3, pt.size=0.5)
In [40]:
bc<-matrix(ncol=4, byrow = T, unlist(strsplit(as.character(colnames(brain@assays$RNA@data)),split=":")))
table(bc[,4])
01 02 03 04 05 06 07 08 09 10 11 12 13 14 15 16 7219 7452 4252 3661 2999 3977 3976 5574 7363 8150 5298 5209 4865 6130 4435 4573
In [41]:
brainregion <- rep("CPU",dim(bc)[1])
brainregion[bc[,4]=="03"|bc[,4]=="04"|bc[,4]=="11"|bc[,4]=="12"]<-"HYP"
brainregion[bc[,4]=="05"|bc[,4]=="06"|bc[,4]=="13"|bc[,4]=="14"]<-"HCa"
brainregion[bc[,4]=="07"|bc[,4]=="08"|bc[,4]=="15"|bc[,4]=="16"]<-"HCp"
table(brainregion)
brainregion CPU HCa HCp HYP 30184 17971 18558 18420
In [42]:
modality <- rep("H3K4me1",dim(bc)[1])
modality[bc[,4]=="09"|bc[,4]=="10"|bc[,4]=="11"|bc[,4]=="12"|bc[,4]=="13"|bc[,4]=="14"|bc[,4]=="15"|bc[,4]=="16"]<-"H3K9me3"
table(modality)
modality H3K4me1 H3K9me3 39110 46023
In [43]:
brain@meta.data<-cbind(brain@meta.data, cbind(brainregion, modality))
oriBarcode<-bc[,4]
table(bc[,1])
sublib <- bc[,1]
brain@meta.data<-cbind(brain@meta.data, cbind(oriBarcode,sublib))
brain.exp2.rna <- brain
rm(brain.data,brain,bc,plot1,plot2,brainregion,modality,oriBarcode,sublib)
B01 B02 B03 B04 B05 B06 B07 B08 B09 B10 B11 B12 B13 B14 B15 B16 1664 1521 1908 1285 2049 2013 1546 2016 1471 1698 1908 1848 1818 1405 1740 2235 B17 B18 B19 B20 B21 B22 B23 B24 B25 B26 B27 B28 B29 B30 B31 B32 1653 1492 1628 1543 1738 1667 1631 1958 2306 1979 2056 2125 2032 1969 2209 2387 B33 B34 B35 B36 B37 B38 B39 B40 B41 B42 B43 B44 B45 2136 2066 2359 2198 2167 2359 2127 1701 1667 1748 1760 2361 1986
In [44]:
ls()
- 'brain.exp1.rna'
- 'brain.exp2.rna'
In [45]:
saveRDS(brain.exp2.rna, file = "02.MouseBrainExp2/merge_mtx/brain.exp2.rna.object.rds")
Experiment 3¶
/projects/ps-renlab2/zhw063/99.MouseBrainPairedTag/03.MouseBrainExp3/merge_mtx/
In [46]:
brain.data <- Read10X(data.dir = "/projects/ps-renlab2/zhw063/99.MouseBrainPairedTag/03.MouseBrainExp3/merge_mtx/MouseBrainExp3_RNA")
In [47]:
brain <- CreateSeuratObject(counts = brain.data, project = "mousebrain", min.cells = 0, min.features = 0)
brain[["percent.mt"]]<-PercentageFeatureSet(brain, pattern="^mt-")
VlnPlot(brain, features=c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol=3, pt.size=0.5)
In [49]:
brain
An object of class Seurat 52159 features across 61457 samples within 1 assay Active assay: RNA (52159 features, 0 variable features)
In [50]:
summary(brain@meta.data$nFeature_RNA)
Min. 1st Qu. Median Mean 3rd Qu. Max.
2 898 1453 1731 2309 7764
In [51]:
summary(brain@meta.data$nCount_RNA)
Min. 1st Qu. Median Mean 3rd Qu. Max.
3 1322 2355 3537 4686 29993
In [52]:
summary(brain@meta.data$percent.mt)
Min. 1st Qu. Median Mean 3rd Qu. Max. 0.0000 0.0000 0.0320 0.4795 0.4797 100.0000
In [53]:
plot1 <- FeatureScatter(brain, feature1 = "nCount_RNA", feature2 = "percent.mt")
plot2 <- FeatureScatter(brain, feature1 = "nCount_RNA", feature2 = "nFeature_RNA")
plot1 + plot2
In [54]:
brain.test <- subset(brain, subset = nFeature_RNA > 500 & nFeature_RNA < 6000 & percent.mt < 2 & nCount_RNA < 20000)
brain
An object of class Seurat 52159 features across 61457 samples within 1 assay Active assay: RNA (52159 features, 0 variable features)
In [55]:
brain.test
An object of class Seurat 52159 features across 56486 samples within 1 assay Active assay: RNA (52159 features, 0 variable features)
In [56]:
brain<-brain.test;rm(brain.test)
VlnPlot(brain, features=c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol=3, pt.size=0.5)
In [57]:
bc<-matrix(ncol=4, byrow = T, unlist(strsplit(as.character(colnames(brain@assays$RNA@data)),split=":")))
table(bc[,4])
01 02 03 04 05 06 07 08 09 10 11 12 13 14 15 16 1854 1340 1250 1062 509 622 1284 2298 541 386 6715 4590 3651 5468 1221 1281 17 18 19 20 21 22 23 24 2097 3544 2228 3195 6220 3337 1407 386
In [58]:
table(bc[,1])
C01 C02 C03 C04 C05 C06 C07 C08 C09 C10 C11 C12 C13 C14 C15 C16 1316 1456 1128 1177 1209 1214 1197 1355 1220 1203 1256 1085 1055 1041 1019 1051 C17 C18 C19 C20 C21 C22 C23 C24 C25 C26 C27 C28 C29 C30 C31 C32 989 1037 1017 1109 1016 1055 1195 1090 1059 1112 855 843 1244 1262 1193 1174 C33 C34 C35 C36 C37 C38 C39 C40 C41 C42 C43 C44 C45 C46 C47 C48 1107 1076 1092 994 992 1023 996 1087 1057 975 1 1301 1126 913 1185 1065 C49 C50 C51 C52 1066 1114 1046 1038
In [59]:
brainregion <- rep("ERC",dim(bc)[1])
brainregion[bc[,4]=="03"|bc[,4]=="04"|bc[,4]=="13"|bc[,4]=="14"]<-"AMY"
brainregion[bc[,4]=="05"|bc[,4]=="06"|bc[,4]=="15"|bc[,4]=="16"]<-"NAC"
brainregion[bc[,4]=="07"|bc[,4]=="08"|bc[,4]=="17"|bc[,4]=="18"]<-"VTA_SnR"
brainregion[bc[,4]=="09"|bc[,4]=="10"|bc[,4]=="19"|bc[,4]=="20"]<-"PFC"
brainregion[bc[,4]=="21"|bc[,4]=="22"|bc[,4]=="23"|bc[,4]=="24"]<-"mESC"
table(brainregion)
brainregion
AMY ERC mESC NAC PFC VTA_SnR
11431 14499 11350 3633 6350 9223
In [60]:
modality <- rep("H3K27ac",dim(bc)[1])
modality[bc[,4]=="11"|bc[,4]=="12"|bc[,4]=="13"|bc[,4]=="14"|bc[,4]=="15"|bc[,4]=="16"|bc[,4]=="17"|bc[,4]=="18"|bc[,4]=="19"|bc[,4]=="20"|bc[,4]=="23"|bc[,4]=="24"]<-"H3K27me3"
table(modality)
modality H3K27ac H3K27me3 20703 35783
In [61]:
brain@meta.data<-cbind(brain@meta.data, cbind(brainregion, modality))
oriBarcode<-bc[,4]
table(bc[,1])
sublib <- bc[,1]
brain@meta.data<-cbind(brain@meta.data, cbind(oriBarcode,sublib))
brain.exp3.rna <- brain
rm(brain.data,brain,bc,plot1,plot2,brainregion,modality,oriBarcode,sublib)
C01 C02 C03 C04 C05 C06 C07 C08 C09 C10 C11 C12 C13 C14 C15 C16 1316 1456 1128 1177 1209 1214 1197 1355 1220 1203 1256 1085 1055 1041 1019 1051 C17 C18 C19 C20 C21 C22 C23 C24 C25 C26 C27 C28 C29 C30 C31 C32 989 1037 1017 1109 1016 1055 1195 1090 1059 1112 855 843 1244 1262 1193 1174 C33 C34 C35 C36 C37 C38 C39 C40 C41 C42 C43 C44 C45 C46 C47 C48 1107 1076 1092 994 992 1023 996 1087 1057 975 1 1301 1126 913 1185 1065 C49 C50 C51 C52 1066 1114 1046 1038
In [62]:
saveRDS(brain.exp3.rna, file = "03.MouseBrainExp3/merge_mtx/brain.exp3.rna.object.rds")
Experiment 4¶
/projects/ps-renlab2/zhw063/99.MouseBrainPairedTag/04.MouseBrainExp4/merge_mtx/
In [76]:
brain.data <- Read10X(data.dir = "/projects/ps-renlab2/zhw063/99.MouseBrainPairedTag/04.MouseBrainExp4/merge_mtx/MouseBrainExp4_RNA")
In [77]:
brain <- CreateSeuratObject(counts = brain.data, project = "mousebrain", min.cells = 0, min.features = 0)
brain[["percent.mt"]]<-PercentageFeatureSet(brain, pattern="^mt-")
VlnPlot(brain, features=c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol=3, pt.size=0.5)
In [78]:
brain
An object of class Seurat 51507 features across 65640 samples within 1 assay Active assay: RNA (51507 features, 0 variable features)
In [79]:
summary(brain@meta.data$nFeature_RNA)
Min. 1st Qu. Median Mean 3rd Qu. Max.
57 908 1244 1420 1725 7607
In [80]:
summary(brain@meta.data$nCount_RNA)
Min. 1st Qu. Median Mean 3rd Qu. Max.
502 1418 2098 2697 3252 29696
In [81]:
summary(brain@meta.data$percent.mt)
Min. 1st Qu. Median Mean 3rd Qu. Max. 0.00000 0.00000 0.00000 0.05371 0.00000 11.18144
In [82]:
plot1 <- FeatureScatter(brain, feature1 = "nCount_RNA", feature2 = "percent.mt")
plot2 <- FeatureScatter(brain, feature1 = "nCount_RNA", feature2 = "nFeature_RNA")
plot1 + plot2
In [83]:
brain.test <- subset(brain, subset = nFeature_RNA > 500 & nFeature_RNA < 6000 & percent.mt < 2 & nCount_RNA < 18000)
brain
An object of class Seurat 51507 features across 65640 samples within 1 assay Active assay: RNA (51507 features, 0 variable features)
In [84]:
brain.test
An object of class Seurat 51507 features across 65065 samples within 1 assay Active assay: RNA (51507 features, 0 variable features)
In [85]:
brain<-brain.test;rm(brain.test)
VlnPlot(brain, features=c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol=3, pt.size=0.5)
In [86]:
bc<-matrix(ncol=4, byrow = T, unlist(strsplit(as.character(colnames(brain@assays$RNA@data)),split=":")))
table(bc[,4])
01 02 03 04 05 06 07 08 09 10 11 12 13 14 15 16 3211 4351 3058 2881 2422 3142 1908 2211 1058 1275 4502 2440 4952 3820 3137 3130 17 18 19 20 21 22 23 24 3148 2669 2380 2531 463 397 3700 2279
In [87]:
table(bc[,1])
D01 D02 D03 D04 D05 D06 D07 D08 D09 D10 D11 D12 D13 D14 D15 D16 1685 1801 1439 1754 1711 1671 1851 1752 1401 1386 1391 1364 1505 1694 1554 1636 D17 D18 D19 D20 D21 D22 D23 D24 D25 D26 D27 D28 D29 D30 D31 D32 1570 1687 1572 1617 1731 1590 1561 1655 1631 1519 1676 1749 1690 1093 1237 1291 D33 D34 D35 D36 D37 D38 D39 D40 D41 1318 1217 1850 1932 1856 1833 1599 1420 1576
In [88]:
brainregion <- rep("ERC",dim(bc)[1])
brainregion[bc[,4]=="03"|bc[,4]=="04"|bc[,4]=="15"|bc[,4]=="16"]<-"AMY"
brainregion[bc[,4]=="05"|bc[,4]=="06"|bc[,4]=="11"|bc[,4]=="17"|bc[,4]=="18"|bc[,4]=="23"]<-"NAC"
brainregion[bc[,4]=="07"|bc[,4]=="08"|bc[,4]=="12"|bc[,4]=="19"|bc[,4]=="20"|bc[,4]=="24"]<-"VTA_SnR"
brainregion[bc[,4]=="09"|bc[,4]=="10"|bc[,4]=="21"|bc[,4]=="22"]<-"PFC"
table(brainregion)
brainregion
AMY ERC NAC PFC VTA_SnR
12206 16334 19583 3193 13749
In [89]:
modality <- rep("H3K4me1",dim(bc)[1])
modality[bc[,4]=="13"|bc[,4]=="14"|bc[,4]=="15"|bc[,4]=="16"|bc[,4]=="17"|bc[,4]=="18"|bc[,4]=="19"|bc[,4]=="20"|bc[,4]=="21"|bc[,4]=="22"|bc[,4]=="23"|bc[,4]=="24"]<-"H3K9me3"
table(modality)
modality H3K4me1 H3K9me3 32459 32606
In [90]:
brain@meta.data<-cbind(brain@meta.data, cbind(brainregion, modality))
oriBarcode<-bc[,4]
table(bc[,1])
sublib <- bc[,1]
brain@meta.data<-cbind(brain@meta.data, cbind(oriBarcode,sublib))
brain.exp4.rna <- brain
rm(brain.data,brain,bc,plot1,plot2,brainregion,modality,oriBarcode,sublib)
D01 D02 D03 D04 D05 D06 D07 D08 D09 D10 D11 D12 D13 D14 D15 D16 1685 1801 1439 1754 1711 1671 1851 1752 1401 1386 1391 1364 1505 1694 1554 1636 D17 D18 D19 D20 D21 D22 D23 D24 D25 D26 D27 D28 D29 D30 D31 D32 1570 1687 1572 1617 1731 1590 1561 1655 1631 1519 1676 1749 1690 1093 1237 1291 D33 D34 D35 D36 D37 D38 D39 D40 D41 1318 1217 1850 1932 1856 1833 1599 1420 1576
In [92]:
brain.exp4.rna <- brain.exp4.1.rna
rm(brain.exp4.1.rna)
saveRDS(brain.exp4.rna, file = "/projects/ps-renlab2/zhw063/99.MouseBrainPairedTag/04.MouseBrainExp4/merge_mtx/brain.exp4.rna.object.rds")
Experiment 5¶
/projects/ps-renlab2/zhw063/99.MouseBrainPairedTag/05.MouseBrainExp5/merge_mtx/MouseBrainExp5_RNA
In [95]:
brain.data <- Read10X(data.dir = "/projects/ps-renlab2/zhw063/99.MouseBrainPairedTag/05.MouseBrainExp5/merge_mtx/MouseBrainExp5_RNA")
In [96]:
brain <- CreateSeuratObject(counts = brain.data, project = "mousebrain", min.cells = 0, min.features = 0)
brain[["percent.mt"]]<-PercentageFeatureSet(brain, pattern="^mt-")
VlnPlot(brain, features=c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol=3, pt.size=0.5)
In [97]:
brain
An object of class Seurat 52811 features across 164754 samples within 1 assay Active assay: RNA (52811 features, 0 variable features)
In [98]:
summary(brain@meta.data$nFeature_RNA)
Min. 1st Qu. Median Mean 3rd Qu. Max.
239 882 1170 1347 1606 7760
In [99]:
summary(brain@meta.data$nCount_RNA)
Min. 1st Qu. Median Mean 3rd Qu. Max.
800 1335 1907 2453 2901 27353
In [100]:
summary(brain@meta.data$percent.mt)
Min. 1st Qu. Median Mean 3rd Qu. Max. 0.0000 0.0000 0.0000 0.1001 0.1224 46.5153
In [101]:
plot1 <- FeatureScatter(brain, feature1 = "nCount_RNA", feature2 = "percent.mt")
plot2 <- FeatureScatter(brain, feature1 = "nCount_RNA", feature2 = "nFeature_RNA")
plot1 + plot2
Rasterizing points since number of points exceeds 100,000. To disable this behavior set `raster=FALSE` Rasterizing points since number of points exceeds 100,000. To disable this behavior set `raster=FALSE`
In [102]:
brain.test <- subset(brain, subset = nFeature_RNA > 500 & nFeature_RNA < 6000 & percent.mt < 2 & nCount_RNA < 18000)
brain
An object of class Seurat 52811 features across 164754 samples within 1 assay Active assay: RNA (52811 features, 0 variable features)
In [103]:
brain.test
An object of class Seurat 52811 features across 164042 samples within 1 assay Active assay: RNA (52811 features, 0 variable features)
In [104]:
brain<-brain.test;rm(brain.test)
VlnPlot(brain, features=c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol=3, pt.size=0.5)
In [105]:
bc<-matrix(ncol=4, byrow = T, unlist(strsplit(as.character(colnames(brain@assays$RNA@data)),split=":")))
table(bc[,4])
01 02 03 04 05 06 07 08 09 10 11 12 13 5919 6114 4019 5090 3561 4211 4136 5405 4460 4484 3279 1838 2112 14 15 16 17 18 19 20 21 22 23 24 25 26 3936 2193 1759 9753 11512 7714 6649 8244 7108 7607 5788 5826 6398 27 28 29 30 31 32 33 34 4316 3364 5489 6610 2157 2983 5 3
In [106]:
table(bc[,1])
E01 E02 E03 E04 E05 E06 E07 E08 E09 E10 E11 E12 E13 E14 E15 E16 2861 3335 3004 2743 1791 1992 1868 2228 2046 2024 1200 2192 2033 2313 1942 2086 E17 E18 E19 E20 E21 E22 E23 E24 E25 E26 E27 E28 E29 E30 E31 E32 1801 2007 1624 1786 2302 2099 1888 1725 1794 1923 1652 2287 2280 2141 2756 2555 E33 E34 E35 E36 E37 E38 E39 E40 E41 E42 E43 E44 E45 E46 E47 E48 2676 2548 2626 1645 1845 2119 2053 2077 2377 2155 2069 1835 2485 2313 2021 2278 E49 E50 E51 E52 E53 E54 E55 E56 E57 E58 E59 E60 E61 E62 E63 E64 2624 2332 2184 2149 2201 2671 2729 2664 1620 620 2902 2332 2268 1857 2396 2049 E65 E66 E67 E68 E69 E70 E71 E72 E73 E74 E75 E76 2333 2472 2189 1755 1705 894 1643 1460 2914 2799 2698 2182
In [107]:
brainregion <- rep("CPU",dim(bc)[1])
brainregion[bc[,4]=="03"|bc[,4]=="04"|bc[,4]=="19"|bc[,4]=="20"]<-"HYP"
brainregion[bc[,4]=="05"|bc[,4]=="06"|bc[,4]=="21"|bc[,4]=="22"]<-"HCa"
brainregion[bc[,4]=="07"|bc[,4]=="08"|bc[,4]=="23"|bc[,4]=="24"]<-"HCp"
brainregion[bc[,4]=="09"|bc[,4]=="10"|bc[,4]=="25"|bc[,4]=="26"]<-"ERC"
brainregion[bc[,4]=="11"|bc[,4]=="12"|bc[,4]=="27"|bc[,4]=="28"]<-"AMY"
brainregion[bc[,4]=="13"|bc[,4]=="14"|bc[,4]=="29"|bc[,4]=="30"]<-"NAC"
brainregion[bc[,4]=="15"|bc[,4]=="16"|bc[,4]=="31"|bc[,4]=="32"]<-"VTA_SnR"
brainregion[bc[,4]=="33"|bc[,4]=="34"]<-"ITGremove"
table(brainregion)
brainregion
AMY CPU ERC HCa HCp HYP ITGremove NAC
12797 33298 21168 23124 22936 23472 8 18147
VTA_SnR
9092
In [108]:
modality <- rep("H3K27ac",dim(bc)[1])
modality[bc[,4]=="17"|bc[,4]=="18"|bc[,4]=="19"|bc[,4]=="20"|bc[,4]=="21"|bc[,4]=="22"|bc[,4]=="23"|bc[,4]=="24"|bc[,4]=="25"|bc[,4]=="26"|bc[,4]=="27"|bc[,4]=="28"|bc[,4]=="29"|bc[,4]=="30"|bc[,4]=="31"|bc[,4]=="32"]<-"H3K27me3"
table(modality)
modality H3K27ac H3K27me3 62524 101518
In [109]:
brain@meta.data<-cbind(brain@meta.data, cbind(brainregion, modality))
oriBarcode<-bc[,4]
table(bc[,1])
sublib <- bc[,1]
brain@meta.data<-cbind(brain@meta.data, cbind(oriBarcode,sublib))
brain.exp5.rna <- brain
rm(brain.data,brain,bc,plot1,plot2,brainregion,modality,oriBarcode,sublib)
E01 E02 E03 E04 E05 E06 E07 E08 E09 E10 E11 E12 E13 E14 E15 E16 2861 3335 3004 2743 1791 1992 1868 2228 2046 2024 1200 2192 2033 2313 1942 2086 E17 E18 E19 E20 E21 E22 E23 E24 E25 E26 E27 E28 E29 E30 E31 E32 1801 2007 1624 1786 2302 2099 1888 1725 1794 1923 1652 2287 2280 2141 2756 2555 E33 E34 E35 E36 E37 E38 E39 E40 E41 E42 E43 E44 E45 E46 E47 E48 2676 2548 2626 1645 1845 2119 2053 2077 2377 2155 2069 1835 2485 2313 2021 2278 E49 E50 E51 E52 E53 E54 E55 E56 E57 E58 E59 E60 E61 E62 E63 E64 2624 2332 2184 2149 2201 2671 2729 2664 1620 620 2902 2332 2268 1857 2396 2049 E65 E66 E67 E68 E69 E70 E71 E72 E73 E74 E75 E76 2333 2472 2189 1755 1705 894 1643 1460 2914 2799 2698 2182
In [110]:
saveRDS(brain.exp5.rna, file = "/projects/ps-renlab2/zhw063/99.MouseBrainPairedTag/05.MouseBrainExp5/merge_mtx/brain.exp5.rna.object.rds")
In [111]:
ls()
- 'brain.exp1.rna'
- 'brain.exp2.rna'
- 'brain.exp3.rna'
- 'brain.exp4.rna'
- 'brain.exp5.rna'
Experiment 6¶
/projects/ps-renlab2/zhw063/99.MouseBrainPairedTag/06.MouseBrainExp6/merge_mtx
In [8]:
brain.data <- Read10X(data.dir = "/projects/ps-renlab2/zhw063/99.MouseBrainPairedTag/06.MouseBrainExp6/merge_mtx/MouseBrainExp6_RNA")
In [113]:
brain <- CreateSeuratObject(counts = brain.data, project = "mousebrain", min.cells = 0, min.features = 0)
brain[["percent.mt"]]<-PercentageFeatureSet(brain, pattern="^mt-")
VlnPlot(brain, features=c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol=3, pt.size=0.5)
In [114]:
brain
An object of class Seurat 49918 features across 50684 samples within 1 assay Active assay: RNA (49918 features, 0 variable features)
In [115]:
summary(brain@meta.data$nFeature_RNA)
Min. 1st Qu. Median Mean 3rd Qu. Max.
428 711 884 1023 1172 7113
In [116]:
summary(brain@meta.data$nCount_RNA)
Min. 1st Qu. Median Mean 3rd Qu. Max.
800 998 1294 1618 1843 25241
In [117]:
summary(brain@meta.data$percent.mt)
Min. 1st Qu. Median Mean 3rd Qu. Max. 0.0000 0.0000 0.0000 0.0631 0.0000 11.2921
In [118]:
plot1 <- FeatureScatter(brain, feature1 = "nCount_RNA", feature2 = "percent.mt")
plot2 <- FeatureScatter(brain, feature1 = "nCount_RNA", feature2 = "nFeature_RNA")
plot1 + plot2
In [119]:
brain.test <- subset(brain, subset = nFeature_RNA > 500 & nFeature_RNA < 6000 & percent.mt < 2 & nCount_RNA < 15000)
brain
An object of class Seurat 49918 features across 50684 samples within 1 assay Active assay: RNA (49918 features, 0 variable features)
In [120]:
brain.test
An object of class Seurat 49918 features across 50540 samples within 1 assay Active assay: RNA (49918 features, 0 variable features)
In [121]:
brain<-brain.test;rm(brain.test)
VlnPlot(brain, features=c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol=3, pt.size=0.5)
In [122]:
bc<-matrix(ncol=4, byrow = T, unlist(strsplit(as.character(colnames(brain@assays$RNA@data)),split=":")))
table(bc[,4])
01 02 03 04 05 06 07 08 09 10 11 12 13 14 15 16 1741 2390 955 1039 1337 1027 1564 2651 2302 2384 1656 1375 3438 3877 1000 840 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 1260 1781 1244 1433 1956 1507 2518 1861 889 999 1009 658 1031 1746 526 546
In [123]:
table(bc[,1])
F01 F02 F03 F04 F05 F06 F07 F08 F09 F10 F11 F12 F13 F14 F15 F16 706 921 772 69 756 700 642 635 816 825 825 816 496 506 529 518 F17 F18 F19 F20 F21 F22 F23 F24 F25 F26 F27 F28 F29 F30 F31 F32 714 492 522 894 698 541 504 443 1838 1705 1756 1742 1679 1724 1808 1480 F33 F34 F35 F36 F37 F38 F39 F40 F41 F42 F43 F44 F45 F46 F47 F48 1530 1458 1524 1452 294 299 493 693 423 409 698 209 418 576 606 391 F49 F50 F51 F52 F53 F54 1495 918 1872 1926 1892 1892
In [124]:
brainregion <- rep("CPU",dim(bc)[1])
brainregion[bc[,4]=="03"|bc[,4]=="04"|bc[,4]=="19"|bc[,4]=="20"]<-"HYP"
brainregion[bc[,4]=="05"|bc[,4]=="06"|bc[,4]=="21"|bc[,4]=="22"]<-"HCa"
brainregion[bc[,4]=="07"|bc[,4]=="08"|bc[,4]=="23"|bc[,4]=="24"]<-"HCp"
brainregion[bc[,4]=="09"|bc[,4]=="10"|bc[,4]=="25"|bc[,4]=="26"]<-"ERC"
brainregion[bc[,4]=="11"|bc[,4]=="12"|bc[,4]=="27"|bc[,4]=="28"]<-"AMY"
brainregion[bc[,4]=="13"|bc[,4]=="14"|bc[,4]=="29"|bc[,4]=="30"]<-"NAC"
brainregion[bc[,4]=="15"|bc[,4]=="16"|bc[,4]=="31"|bc[,4]=="32"]<-"VTA_SnR"
table(brainregion)
brainregion
AMY CPU ERC HCa HCp HYP NAC VTA_SnR
4698 7172 6574 5827 8594 4671 10092 2912
In [125]:
modality <- rep("H3K4me1",dim(bc)[1])
modality[bc[,4]=="17"|bc[,4]=="18"|bc[,4]=="19"|bc[,4]=="20"|bc[,4]=="21"|bc[,4]=="22"|bc[,4]=="23"|bc[,4]=="24"|bc[,4]=="25"|bc[,4]=="26"|bc[,4]=="27"|bc[,4]=="28"|bc[,4]=="29"|bc[,4]=="30"|bc[,4]=="31"|bc[,4]=="32"]<-"H3K9me3"
table(modality)
modality H3K4me1 H3K9me3 29576 20964
In [126]:
brain@meta.data<-cbind(brain@meta.data, cbind(brainregion, modality))
oriBarcode<-bc[,4]
table(bc[,1])
sublib <- bc[,1]
brain@meta.data<-cbind(brain@meta.data, cbind(oriBarcode,sublib))
brain.exp6.rna <- brain
rm(brain.data,brain,bc,plot1,plot2,brainregion,modality,oriBarcode,sublib)
F01 F02 F03 F04 F05 F06 F07 F08 F09 F10 F11 F12 F13 F14 F15 F16 706 921 772 69 756 700 642 635 816 825 825 816 496 506 529 518 F17 F18 F19 F20 F21 F22 F23 F24 F25 F26 F27 F28 F29 F30 F31 F32 714 492 522 894 698 541 504 443 1838 1705 1756 1742 1679 1724 1808 1480 F33 F34 F35 F36 F37 F38 F39 F40 F41 F42 F43 F44 F45 F46 F47 F48 1530 1458 1524 1452 294 299 493 693 423 409 698 209 418 576 606 391 F49 F50 F51 F52 F53 F54 1495 918 1872 1926 1892 1892
In [127]:
ls()
- 'brain.exp1.rna'
- 'brain.exp2.rna'
- 'brain.exp3.rna'
- 'brain.exp4.rna'
- 'brain.exp5.rna'
- 'brain.exp6.rna'
In [128]:
saveRDS(brain.exp6.rna, file = "/projects/ps-renlab2/zhw063/99.MouseBrainPairedTag/06.MouseBrainExp6/merge_mtx/brain.exp6.rna.object.rds")
Experiment 7¶
/projects/ps-renlab2/zhw063/99.MouseBrainPairedTag/07.MouseBrainExp7/merge_mtx
In [129]:
brain.data <- Read10X(data.dir = "/projects/ps-renlab2/zhw063/99.MouseBrainPairedTag/07.MouseBrainExp7/merge_mtx/MouseBrainExp7_RNA")
In [130]:
brain <- CreateSeuratObject(counts = brain.data, project = "mousebrain", min.cells = 0, min.features = 0)
brain[["percent.mt"]]<-PercentageFeatureSet(brain, pattern="^mt-")
VlnPlot(brain, features=c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol=3, pt.size=0.5)
In [131]:
brain
An object of class Seurat 52563 features across 109718 samples within 1 assay Active assay: RNA (52563 features, 0 variable features)
In [132]:
summary(brain@meta.data$nFeature_RNA)
Min. 1st Qu. Median Mean 3rd Qu. Max.
304 840 1138 1363 1619 7995
In [133]:
summary(brain@meta.data$nCount_RNA)
Min. 1st Qu. Median Mean 3rd Qu. Max.
800 1244 1811 2441 2836 29471
In [134]:
summary(brain@meta.data$percent.mt)
Min. 1st Qu. Median Mean 3rd Qu. Max. 0.0000 0.0000 0.0000 0.2052 0.2020 14.0278
In [135]:
plot1 <- FeatureScatter(brain, feature1 = "nCount_RNA", feature2 = "percent.mt")
plot2 <- FeatureScatter(brain, feature1 = "nCount_RNA", feature2 = "nFeature_RNA")
plot1 + plot2
Rasterizing points since number of points exceeds 100,000. To disable this behavior set `raster=FALSE` Rasterizing points since number of points exceeds 100,000. To disable this behavior set `raster=FALSE`
In [136]:
brain.test <- subset(brain, subset = nFeature_RNA > 500 & nFeature_RNA < 6000 & percent.mt < 2 & nCount_RNA < 20000)
brain
An object of class Seurat 52563 features across 109718 samples within 1 assay Active assay: RNA (52563 features, 0 variable features)
In [137]:
brain.test
An object of class Seurat 52563 features across 107313 samples within 1 assay Active assay: RNA (52563 features, 0 variable features)
In [138]:
brain<-brain.test;rm(brain.test)
VlnPlot(brain, features=c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol=3, pt.size=0.5)
In [139]:
bc<-matrix(ncol=4, byrow = T, unlist(strsplit(as.character(colnames(brain@assays$RNA@data)),split=":")))
table(bc[,4])
01 02 03 04 05 06 07 08 09 10 11 12 13 14 15 16 3499 3819 2409 2601 798 1416 1392 2814 4621 4366 1839 1930 1322 2089 1941 1724 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 8418 9347 5641 5445 1804 1984 4571 3935 5661 6049 4153 4170 1698 1993 1684 2180
In [140]:
table(bc[,1])
G01 G02 G03 G04 G05 G06 G07 G08 G09 G10 G11 G12 G13 G14 G15 G16 1637 1812 1904 1710 1617 1542 1499 1466 1628 1611 1553 1778 1566 1547 1626 1557 G17 G18 G19 G20 G21 G22 G23 G24 G25 G26 G27 G28 G29 G30 G31 G32 1530 1411 1429 1762 1771 1539 1621 1742 1760 1676 1530 1213 1346 1529 1587 1090 G33 G34 G35 G36 G37 G38 G39 G40 G41 G42 G43 G44 G45 G46 G47 G48 1403 1190 1767 1595 1610 1458 1081 1460 636 1704 1303 1144 583 1471 1482 1471 G49 G50 G51 G52 G53 G54 G55 G56 G57 G58 G59 G60 G61 G62 G63 G64 1394 1323 1094 1312 911 1404 1448 1449 1440 1382 1482 1456 1321 853 1369 1263 G65 G66 G67 G68 G69 G70 G71 G72 G73 G74 G75 1604 1437 715 1438 763 1011 347 1910 1886 1702 1652
In [141]:
brainregion <- rep("CPU",dim(bc)[1])
brainregion[bc[,4]=="03"|bc[,4]=="04"|bc[,4]=="19"|bc[,4]=="20"]<-"HYP"
brainregion[bc[,4]=="05"|bc[,4]=="06"|bc[,4]=="21"|bc[,4]=="22"]<-"HCa"
brainregion[bc[,4]=="07"|bc[,4]=="08"|bc[,4]=="23"|bc[,4]=="24"]<-"HCp"
brainregion[bc[,4]=="09"|bc[,4]=="10"|bc[,4]=="25"|bc[,4]=="26"]<-"ERC"
brainregion[bc[,4]=="11"|bc[,4]=="12"|bc[,4]=="27"|bc[,4]=="28"]<-"AMY"
brainregion[bc[,4]=="13"|bc[,4]=="14"|bc[,4]=="29"|bc[,4]=="30"]<-"NAC"
brainregion[bc[,4]=="15"|bc[,4]=="16"|bc[,4]=="31"|bc[,4]=="32"]<-"VTA_SnR"
table(brainregion)
brainregion
AMY CPU ERC HCa HCp HYP NAC VTA_SnR
12092 25083 20697 6002 12712 16096 7102 7529
In [142]:
modality <- rep("H3K27ac",dim(bc)[1])
modality[bc[,4]=="17"|bc[,4]=="18"|bc[,4]=="19"|bc[,4]=="20"|bc[,4]=="21"|bc[,4]=="22"|bc[,4]=="23"|bc[,4]=="24"|bc[,4]=="25"|bc[,4]=="26"|bc[,4]=="27"|bc[,4]=="28"|bc[,4]=="29"|bc[,4]=="30"|bc[,4]=="31"|bc[,4]=="32"]<-"H3K27me3"
table(modality)
modality H3K27ac H3K27me3 38580 68733
In [143]:
brain@meta.data<-cbind(brain@meta.data, cbind(brainregion, modality))
oriBarcode<-bc[,4]
table(bc[,1])
sublib <- bc[,1]
brain@meta.data<-cbind(brain@meta.data, cbind(oriBarcode,sublib))
brain.exp7.rna <- brain
rm(brain.data,brain,bc,plot1,plot2,brainregion,modality,oriBarcode,sublib)
G01 G02 G03 G04 G05 G06 G07 G08 G09 G10 G11 G12 G13 G14 G15 G16 1637 1812 1904 1710 1617 1542 1499 1466 1628 1611 1553 1778 1566 1547 1626 1557 G17 G18 G19 G20 G21 G22 G23 G24 G25 G26 G27 G28 G29 G30 G31 G32 1530 1411 1429 1762 1771 1539 1621 1742 1760 1676 1530 1213 1346 1529 1587 1090 G33 G34 G35 G36 G37 G38 G39 G40 G41 G42 G43 G44 G45 G46 G47 G48 1403 1190 1767 1595 1610 1458 1081 1460 636 1704 1303 1144 583 1471 1482 1471 G49 G50 G51 G52 G53 G54 G55 G56 G57 G58 G59 G60 G61 G62 G63 G64 1394 1323 1094 1312 911 1404 1448 1449 1440 1382 1482 1456 1321 853 1369 1263 G65 G66 G67 G68 G69 G70 G71 G72 G73 G74 G75 1604 1437 715 1438 763 1011 347 1910 1886 1702 1652
In [144]:
ls()
- 'brain.exp1.rna'
- 'brain.exp2.rna'
- 'brain.exp3.rna'
- 'brain.exp4.rna'
- 'brain.exp5.rna'
- 'brain.exp6.rna'
- 'brain.exp7.rna'
In [145]:
saveRDS(brain.exp7.rna, file = "/projects/ps-renlab2/zhw063/99.MouseBrainPairedTag/07.MouseBrainExp7/merge_mtx/brain.exp7.rna.object.rds")
Experiment 8¶
/projects/ps-renlab2/zhw063/99.MouseBrainPairedTag/08.MouseBrainExp8/merge_mtx
In [146]:
brain.data <- Read10X(data.dir = "/projects/ps-renlab2/zhw063/99.MouseBrainPairedTag/08.MouseBrainExp8/merge_mtx/MouseBrainExp8_RNA")
In [147]:
brain <- CreateSeuratObject(counts = brain.data, project = "mousebrain", min.cells = 0, min.features = 0)
brain[["percent.mt"]]<-PercentageFeatureSet(brain, pattern="^mt-")
VlnPlot(brain, features=c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol=3, pt.size=0.5)
In [148]:
brain
An object of class Seurat 52547 features across 101018 samples within 1 assay Active assay: RNA (52547 features, 0 variable features)
In [149]:
summary(brain@meta.data$nFeature_RNA)
Min. 1st Qu. Median Mean 3rd Qu. Max.
261 1204 1644 1825 2210 7908
In [150]:
summary(brain@meta.data$nCount_RNA)
Min. 1st Qu. Median Mean 3rd Qu. Max. 1000 1953 2951 3658 4417 29397
In [151]:
summary(brain@meta.data$percent.mt)
Min. 1st Qu. Median Mean 3rd Qu. Max. 0.0000 0.0000 0.0000 0.1243 0.1100 7.1623
In [152]:
plot1 <- FeatureScatter(brain, feature1 = "nCount_RNA", feature2 = "percent.mt")
plot2 <- FeatureScatter(brain, feature1 = "nCount_RNA", feature2 = "nFeature_RNA")
plot1 + plot2
Rasterizing points since number of points exceeds 100,000. To disable this behavior set `raster=FALSE` Rasterizing points since number of points exceeds 100,000. To disable this behavior set `raster=FALSE`
In [153]:
brain.test <- subset(brain, subset = nFeature_RNA > 500 & nFeature_RNA < 6000 & percent.mt < 2 & nCount_RNA < 20000)
brain
An object of class Seurat 52547 features across 101018 samples within 1 assay Active assay: RNA (52547 features, 0 variable features)
In [154]:
brain.test
An object of class Seurat 52547 features across 99758 samples within 1 assay Active assay: RNA (52547 features, 0 variable features)
In [155]:
brain<-brain.test;rm(brain.test)
VlnPlot(brain, features=c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol=3, pt.size=0.5)
In [157]:
bc<-matrix(ncol=4, byrow = T, unlist(strsplit(as.character(colnames(brain@assays$RNA@data)),split=":")))
table(bc[,4])
01 02 03 04 05 06 07 08 09 10 11 12 13 5296 5173 3771 3279 2719 4149 2756 3261 11353 10388 7572 6346 8789 14 15 16 10933 7089 6884
In [158]:
table(bc[,1])
H01 H02 H03 H04 H05 H06 H07 H08 H09 H10 H11 H12 H13 H14 H15 H16 1559 1587 1561 1154 1593 1456 1720 1509 1511 1590 1524 1348 1504 1568 1693 1434 H17 H18 H19 H20 H21 H22 H23 H24 H25 H26 H27 H28 H29 H30 H31 H32 1515 1514 1532 1627 1634 1385 1517 1667 1727 1787 1824 1837 1705 1589 1529 1752 H33 H34 H35 H36 H37 H38 H39 H40 H41 H42 H43 H44 H45 H46 H47 H48 1694 2714 1827 1665 1566 1450 1679 1698 1580 1560 1564 1608 1471 1522 1635 1660 H49 H50 H51 H52 H53 H54 H55 H56 H57 H58 H59 H60 H61 H62 1473 1724 1521 1371 1540 1569 1800 1692 1467 1781 1472 1433 1507 2063
In [159]:
brainregion <- rep("CPU",dim(bc)[1])
brainregion[bc[,4]=="03"|bc[,4]=="04"|bc[,4]=="11"|bc[,4]=="12"]<-"HYP"
brainregion[bc[,4]=="05"|bc[,4]=="06"|bc[,4]=="13"|bc[,4]=="14"]<-"HCa"
brainregion[bc[,4]=="07"|bc[,4]=="08"|bc[,4]=="15"|bc[,4]=="16"]<-"HCp"
table(brainregion)
brainregion CPU HCa HCp HYP 32210 26590 19990 20968
In [160]:
modality <- rep("H3K27ac",dim(bc)[1])
modality[bc[,4]=="09"|bc[,4]=="10"|bc[,4]=="11"|bc[,4]=="12"|bc[,4]=="13"|bc[,4]=="14"|bc[,4]=="15"|bc[,4]=="16"]<-"H3K27me3"
table(modality)
modality H3K27ac H3K27me3 30404 69354
In [161]:
brain@meta.data<-cbind(brain@meta.data, cbind(brainregion, modality))
oriBarcode<-bc[,4]
table(bc[,1])
sublib <- bc[,1]
brain@meta.data<-cbind(brain@meta.data, cbind(oriBarcode,sublib))
brain.exp8.rna <- brain
rm(brain.data,brain,bc,plot1,plot2,brainregion,modality,oriBarcode,sublib)
H01 H02 H03 H04 H05 H06 H07 H08 H09 H10 H11 H12 H13 H14 H15 H16 1559 1587 1561 1154 1593 1456 1720 1509 1511 1590 1524 1348 1504 1568 1693 1434 H17 H18 H19 H20 H21 H22 H23 H24 H25 H26 H27 H28 H29 H30 H31 H32 1515 1514 1532 1627 1634 1385 1517 1667 1727 1787 1824 1837 1705 1589 1529 1752 H33 H34 H35 H36 H37 H38 H39 H40 H41 H42 H43 H44 H45 H46 H47 H48 1694 2714 1827 1665 1566 1450 1679 1698 1580 1560 1564 1608 1471 1522 1635 1660 H49 H50 H51 H52 H53 H54 H55 H56 H57 H58 H59 H60 H61 H62 1473 1724 1521 1371 1540 1569 1800 1692 1467 1781 1472 1433 1507 2063
In [162]:
saveRDS(brain.exp8.rna, file = "/projects/ps-renlab2/zhw063/99.MouseBrainPairedTag/08.MouseBrainExp8/merge_mtx/brain.exp8.rna.object.rds")
In [163]:
VlnPlot(brain.exp8.rna, features=c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol=3, pt.size=0)
Experiment 9¶
/projects/ps-renlab2/zhw063/99.MouseBrainPairedTag/09.MouseBrainExp9/merge_mtx
In [164]:
brain.data <- Read10X(data.dir = "/projects/ps-renlab2/zhw063/99.MouseBrainPairedTag/09.MouseBrainExp9/merge_mtx/MouseBrainExp9_RNA")
In [165]:
brain <- CreateSeuratObject(counts = brain.data, project = "mousebrain", min.cells = 0, min.features = 0)
brain[["percent.mt"]]<-PercentageFeatureSet(brain, pattern="^mt-")
options(repr.plot.width=16, repr.plot.height=10)
VlnPlot(brain, features=c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol=3, pt.size=0.5)
In [166]:
brain
An object of class Seurat 53144 features across 110223 samples within 1 assay Active assay: RNA (53144 features, 0 variable features)
In [167]:
summary(brain@meta.data$nFeature_RNA)
Min. 1st Qu. Median Mean 3rd Qu. Max.
336 1071 1519 1753 2133 7806
In [168]:
summary(brain@meta.data$nCount_RNA)
Min. 1st Qu. Median Mean 3rd Qu. Max.
800 1672 2626 3505 4169 29949
In [169]:
summary(brain@meta.data$percent.mt)
Min. 1st Qu. Median Mean 3rd Qu. Max. 0.0000 0.0000 0.1638 0.3811 0.4986 28.8601
In [170]:
plot1 <- FeatureScatter(brain, feature1 = "nCount_RNA", feature2 = "percent.mt")
plot2 <- FeatureScatter(brain, feature1 = "nCount_RNA", feature2 = "nFeature_RNA")
plot1 + plot2
Rasterizing points since number of points exceeds 100,000. To disable this behavior set `raster=FALSE` Rasterizing points since number of points exceeds 100,000. To disable this behavior set `raster=FALSE`
In [173]:
brain.test <- subset(brain, subset = nFeature_RNA > 500 & nFeature_RNA < 7000 & percent.mt < 2 & nCount_RNA < 25000)
brain
An object of class Seurat 53144 features across 110223 samples within 1 assay Active assay: RNA (53144 features, 0 variable features)
In [174]:
brain.test
An object of class Seurat 53144 features across 102154 samples within 1 assay Active assay: RNA (53144 features, 0 variable features)
In [175]:
brain<-brain.test;rm(brain.test)
VlnPlot(brain, features=c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol=3, pt.size=0.5)
In [176]:
bc<-matrix(ncol=4, byrow = T, unlist(strsplit(as.character(colnames(brain@assays$RNA@data)),split=":")))
table(bc[,4])
01 02 03 04 05 06 07 08 09 10 11 12 13 14 15 16 8658 8811 6312 6915 4942 5914 3646 3924 6404 9062 7125 7275 7733 7687 4114 3632
In [177]:
table(bc[,1])
I01 I02 I03 I04 I05 I06 I07 I08 I09 I10 I11 I12 I13 I14 I15 I16 1510 1548 1468 1520 1457 1402 945 1230 1219 1228 1196 1220 1292 1440 1376 1285 I17 I18 I19 I20 I21 I22 I23 I24 I25 I26 I27 I28 I29 I30 I31 I32 1252 1261 1167 1184 1093 941 1089 1226 1112 776 915 1193 1197 1262 1177 1240 I33 I34 I35 I36 I37 I38 I39 I40 I41 I42 I43 I44 I45 I46 I47 I48 1043 1157 1175 1162 1267 1277 1306 1326 1376 1290 1077 1327 1420 834 721 1101 I49 I50 I51 I52 I53 I54 I55 I56 I57 I58 I59 I60 I61 I62 I63 I64 1343 1214 1219 1235 1032 1023 1279 1047 1139 1040 901 956 1246 881 1180 1212 I65 I66 I67 I68 I69 I70 I71 I72 I73 I74 I75 I76 I77 I78 I79 I80 1235 1235 1258 1225 1139 1283 1447 1263 1244 1167 1164 5 1229 1182 3 1365 I81 I82 I83 I84 I85 I86 I87 1302 1233 1370 1241 1174 1062 1101
In [178]:
brainregion <- rep("ERC",dim(bc)[1])
brainregion[bc[,4]=="03"|bc[,4]=="04"|bc[,4]=="11"|bc[,4]=="12"]<-"AMY"
brainregion[bc[,4]=="05"|bc[,4]=="06"|bc[,4]=="13"|bc[,4]=="14"]<-"NAC"
brainregion[bc[,4]=="07"|bc[,4]=="08"|bc[,4]=="15"|bc[,4]=="16"]<-"VTA_SnR"
table(brainregion)
brainregion
AMY ERC NAC VTA_SnR
27627 32935 26276 15316
In [179]:
modality <- rep("H3K4me1",dim(bc)[1])
modality[bc[,4]=="09"|bc[,4]=="10"|bc[,4]=="11"|bc[,4]=="12"|bc[,4]=="13"|bc[,4]=="14"|bc[,4]=="15"|bc[,4]=="16"]<-"H3K9me3"
table(modality)
modality H3K4me1 H3K9me3 49122 53032
In [180]:
brain@meta.data<-cbind(brain@meta.data, cbind(brainregion, modality))
oriBarcode<-bc[,4]
table(bc[,1])
sublib <- bc[,1]
brain@meta.data<-cbind(brain@meta.data, cbind(oriBarcode,sublib))
brain.exp9.rna <- brain
rm(brain.data,brain,bc,plot1,plot2,brainregion,modality,oriBarcode,sublib)
I01 I02 I03 I04 I05 I06 I07 I08 I09 I10 I11 I12 I13 I14 I15 I16 1510 1548 1468 1520 1457 1402 945 1230 1219 1228 1196 1220 1292 1440 1376 1285 I17 I18 I19 I20 I21 I22 I23 I24 I25 I26 I27 I28 I29 I30 I31 I32 1252 1261 1167 1184 1093 941 1089 1226 1112 776 915 1193 1197 1262 1177 1240 I33 I34 I35 I36 I37 I38 I39 I40 I41 I42 I43 I44 I45 I46 I47 I48 1043 1157 1175 1162 1267 1277 1306 1326 1376 1290 1077 1327 1420 834 721 1101 I49 I50 I51 I52 I53 I54 I55 I56 I57 I58 I59 I60 I61 I62 I63 I64 1343 1214 1219 1235 1032 1023 1279 1047 1139 1040 901 956 1246 881 1180 1212 I65 I66 I67 I68 I69 I70 I71 I72 I73 I74 I75 I76 I77 I78 I79 I80 1235 1235 1258 1225 1139 1283 1447 1263 1244 1167 1164 5 1229 1182 3 1365 I81 I82 I83 I84 I85 I86 I87 1302 1233 1370 1241 1174 1062 1101
In [181]:
ls()
- 'brain.exp1.rna'
- 'brain.exp2.rna'
- 'brain.exp3.rna'
- 'brain.exp4.rna'
- 'brain.exp5.rna'
- 'brain.exp6.rna'
- 'brain.exp7.rna'
- 'brain.exp8.rna'
- 'brain.exp9.rna'
In [182]:
saveRDS(brain.exp9.rna, file = "/projects/ps-renlab2/zhw063/99.MouseBrainPairedTag/09.MouseBrainExp9/merge_mtx/brain.exp9.rna.object.rds")
Experiment 10¶
/projects/ps-renlab2/zhw063/99.MouseBrainPairedTag/10.MouseBrainExp10/merge_mtx
In [33]:
brain.data <- Read10X(data.dir = "/projects/ps-renlab2/zhw063/99.MouseBrainPairedTag/10.MouseBrainExp10/merge_mtx/MouseBrainExp10_RNA")
In [34]:
brain <- CreateSeuratObject(counts = brain.data, project = "mousebrain", min.cells = 0, min.features = 0)
brain[["percent.mt"]]<-PercentageFeatureSet(brain, pattern="^mt-")
options(repr.plot.width=16, repr.plot.height=10)
VlnPlot(brain, features=c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol=3, pt.size=0.5)
In [35]:
brain
An object of class Seurat 54029 features across 290298 samples within 1 assay Active assay: RNA (54029 features, 0 variable features)
In [36]:
summary(brain@meta.data$nFeature_RNA)
Min. 1st Qu. Median Mean 3rd Qu. Max.
249 965 1364 1548 1894 8044
In [37]:
summary(brain@meta.data$nCount_RNA)
Min. 1st Qu. Median Mean 3rd Qu. Max.
800 1489 2319 2963 3609 29860
In [38]:
summary(brain@meta.data$percent.mt)
Min. 1st Qu. Median Mean 3rd Qu. Max. 0.0000 0.0000 0.0000 0.1150 0.1017 11.7647
In [39]:
plot1 <- FeatureScatter(brain, feature1 = "nCount_RNA", feature2 = "percent.mt")
plot2 <- FeatureScatter(brain, feature1 = "nCount_RNA", feature2 = "nFeature_RNA")
plot1 + plot2
Rasterizing points since number of points exceeds 100,000. To disable this behavior set `raster=FALSE` Rasterizing points since number of points exceeds 100,000. To disable this behavior set `raster=FALSE`
In [40]:
brain.test <- subset(brain, subset = nFeature_RNA > 500 & nFeature_RNA < 65000 & percent.mt < 2 & nCount_RNA < 22000)
brain
An object of class Seurat 54029 features across 290298 samples within 1 assay Active assay: RNA (54029 features, 0 variable features)
In [41]:
brain.test
An object of class Seurat 54029 features across 286685 samples within 1 assay Active assay: RNA (54029 features, 0 variable features)
In [42]:
brain<-brain.test;rm(brain.test)
VlnPlot(brain, features=c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol=3, pt.size=0.5)
In [43]:
bc<-matrix(ncol=4, byrow = T, unlist(strsplit(as.character(colnames(brain@assays$RNA@data)),split=":")))
table(bc[,4])
01 02 03 04 05 06 07 08 09 10 11 12 13 11843 12803 10924 11134 7147 9475 7368 9215 5108 3321 9877 10057 10846 14 15 16 17 18 19 20 21 22 23 24 25 26 11878 7971 7693 12509 12908 11997 9113 9857 9858 8897 7112 4719 4261 27 28 29 30 31 32 8395 9543 9200 11851 4357 5448
In [44]:
table(bc[,1])
J01 J02 J03 J04 J05 J06 J07 J08 J09 J10 J11 J12 J13 J14 J15 J16 2119 2219 2103 1991 2099 2132 1915 2003 1937 1956 1971 2034 2159 2192 2082 1939 J17 J18 J19 J20 J21 J22 J23 J24 J25 J26 J27 J28 J29 J30 J31 J32 2138 1212 1929 1932 2030 2017 1585 2216 2268 2127 2257 2036 2120 2809 2266 2014 J33 J34 J35 J36 J37 J38 J39 J40 J41 J42 J43 J44 J45 J46 J47 J48 2302 2060 2330 2015 2005 2134 2160 2002 2102 2271 2103 2473 2310 2392 2145 1995 J49 J50 J51 J52 J53 J54 J55 J56 J57 J58 J59 J60 J61 J62 J63 J64 2171 2432 2441 2331 2349 2169 2185 1996 1849 2030 2127 2105 2221 2393 1725 1822 J65 J66 J67 J68 J69 J70 J71 J72 J73 J74 J75 J76 J77 J78 J79 J80 2213 2442 1703 2107 2300 1929 2111 1976 1892 2041 2207 2094 2189 2030 1908 2133 J81 J82 J83 J84 J85 J86 J87 J88 J89 J90 J91 J92 J93 J94 J95 J96 2251 2430 2117 2100 2083 2142 1829 1734 1637 2026 1830 1812 1890 1838 1420 1455 J97 J98 J99 O01 O02 O03 O04 O05 O06 O07 O08 O09 O10 O11 O12 O13 1503 1965 1971 2002 2117 2273 2178 1935 2014 1929 2084 2213 2132 2271 2183 2268 O14 O15 O16 O17 O18 O19 O20 O21 O22 O23 O24 O25 O26 O27 O28 O29 2126 1967 2058 2098 2065 1864 1945 1927 2148 2162 2127 2033 2060 2076 2128 2037 O30 O31 O32 O33 O34 O35 O36 O37 O38 O39 O40 2024 2016 2035 2011 1717 1859 2035 2183 1966 2093 2066
In [45]:
brainregion <- rep("CPU",dim(bc)[1])
brainregion[bc[,4]=="03"|bc[,4]=="04"|bc[,4]=="19"|bc[,4]=="20"]<-"HYP"
brainregion[bc[,4]=="05"|bc[,4]=="06"|bc[,4]=="21"|bc[,4]=="22"]<-"HCa"
brainregion[bc[,4]=="07"|bc[,4]=="08"|bc[,4]=="23"|bc[,4]=="24"]<-"HCp"
brainregion[bc[,4]=="09"|bc[,4]=="10"|bc[,4]=="25"|bc[,4]=="26"]<-"ERC"
brainregion[bc[,4]=="11"|bc[,4]=="12"|bc[,4]=="27"|bc[,4]=="28"]<-"AMY"
brainregion[bc[,4]=="13"|bc[,4]=="14"|bc[,4]=="29"|bc[,4]=="30"]<-"NAC"
brainregion[bc[,4]=="15"|bc[,4]=="16"|bc[,4]=="31"|bc[,4]=="32"]<-"VTA_SnR"
table(brainregion)
brainregion
AMY CPU ERC HCa HCp HYP NAC VTA_SnR
37872 50063 17409 36337 32592 43168 43775 25469
In [46]:
modality <- rep("H3K4me1",dim(bc)[1])
modality[bc[,4]=="17"|bc[,4]=="18"|bc[,4]=="19"|bc[,4]=="20"|bc[,4]=="21"|bc[,4]=="22"|bc[,4]=="23"|bc[,4]=="24"|bc[,4]=="25"|bc[,4]=="26"|bc[,4]=="27"|bc[,4]=="28"|bc[,4]=="29"|bc[,4]=="30"|bc[,4]=="31"|bc[,4]=="32"]<-"H3K9me3"
table(modality)
modality H3K4me1 H3K9me3 146660 140025
In [47]:
brain@meta.data<-cbind(brain@meta.data, cbind(brainregion, modality))
oriBarcode<-bc[,4]
table(bc[,1])
sublib <- bc[,1]
brain@meta.data<-cbind(brain@meta.data, cbind(oriBarcode,sublib))
brain.exp10.rna <- brain
rm(brain.data,brain,bc,plot1,plot2,brainregion,modality,oriBarcode,sublib)
J01 J02 J03 J04 J05 J06 J07 J08 J09 J10 J11 J12 J13 J14 J15 J16 2119 2219 2103 1991 2099 2132 1915 2003 1937 1956 1971 2034 2159 2192 2082 1939 J17 J18 J19 J20 J21 J22 J23 J24 J25 J26 J27 J28 J29 J30 J31 J32 2138 1212 1929 1932 2030 2017 1585 2216 2268 2127 2257 2036 2120 2809 2266 2014 J33 J34 J35 J36 J37 J38 J39 J40 J41 J42 J43 J44 J45 J46 J47 J48 2302 2060 2330 2015 2005 2134 2160 2002 2102 2271 2103 2473 2310 2392 2145 1995 J49 J50 J51 J52 J53 J54 J55 J56 J57 J58 J59 J60 J61 J62 J63 J64 2171 2432 2441 2331 2349 2169 2185 1996 1849 2030 2127 2105 2221 2393 1725 1822 J65 J66 J67 J68 J69 J70 J71 J72 J73 J74 J75 J76 J77 J78 J79 J80 2213 2442 1703 2107 2300 1929 2111 1976 1892 2041 2207 2094 2189 2030 1908 2133 J81 J82 J83 J84 J85 J86 J87 J88 J89 J90 J91 J92 J93 J94 J95 J96 2251 2430 2117 2100 2083 2142 1829 1734 1637 2026 1830 1812 1890 1838 1420 1455 J97 J98 J99 O01 O02 O03 O04 O05 O06 O07 O08 O09 O10 O11 O12 O13 1503 1965 1971 2002 2117 2273 2178 1935 2014 1929 2084 2213 2132 2271 2183 2268 O14 O15 O16 O17 O18 O19 O20 O21 O22 O23 O24 O25 O26 O27 O28 O29 2126 1967 2058 2098 2065 1864 1945 1927 2148 2162 2127 2033 2060 2076 2128 2037 O30 O31 O32 O33 O34 O35 O36 O37 O38 O39 O40 2024 2016 2035 2011 1717 1859 2035 2183 1966 2093 2066
In [48]:
ls()
- 'brain.exp10.rna'
- 'brain.exp11.rna'
In [49]:
saveRDS(brain.exp10.rna, file = "/projects/ps-renlab2/zhw063/99.MouseBrainPairedTag/10.MouseBrainExp10/merge_mtx/brain.exp10.rna.object.rds")
Experiment 11¶
/projects/ps-renlab2/zhw063/99.MouseBrainPairedTag/11.MouseBrainExp11/merge_mtx
In [50]:
brain.data <- Read10X(data.dir = "/projects/ps-renlab2/zhw063/99.MouseBrainPairedTag/11.MouseBrainExp11/merge_mtx/MouseBrainExp11_RNA")
In [51]:
brain <- CreateSeuratObject(counts = brain.data, project = "mousebrain", min.cells = 0, min.features = 0)
brain[["percent.mt"]]<-PercentageFeatureSet(brain, pattern="^mt-")
options(repr.plot.width=16, repr.plot.height=10)
VlnPlot(brain, features=c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol=3, pt.size=0.5)
In [52]:
brain
An object of class Seurat 52671 features across 134099 samples within 1 assay Active assay: RNA (52671 features, 0 variable features)
In [53]:
summary(brain@meta.data$nFeature_RNA)
Min. 1st Qu. Median Mean 3rd Qu. Max.
211 790 1046 1255 1468 7805
In [54]:
summary(brain@meta.data$nCount_RNA)
Min. 1st Qu. Median Mean 3rd Qu. Max.
800 1189 1701 2301 2640 29966
In [55]:
summary(brain@meta.data$percent.mt)
Min. 1st Qu. Median Mean 3rd Qu. Max. 0.00000 0.00000 0.00000 0.07582 0.00000 20.78544
In [56]:
plot1 <- FeatureScatter(brain, feature1 = "nCount_RNA", feature2 = "percent.mt")
plot2 <- FeatureScatter(brain, feature1 = "nCount_RNA", feature2 = "nFeature_RNA")
plot1 + plot2
Rasterizing points since number of points exceeds 100,000. To disable this behavior set `raster=FALSE` Rasterizing points since number of points exceeds 100,000. To disable this behavior set `raster=FALSE`
In [57]:
brain.test <- subset(brain, subset = nFeature_RNA > 500 & nFeature_RNA < 6000 & percent.mt < 2 & nCount_RNA < 18000)
brain
An object of class Seurat 52671 features across 134099 samples within 1 assay Active assay: RNA (52671 features, 0 variable features)
In [58]:
brain.test
An object of class Seurat 52671 features across 132932 samples within 1 assay Active assay: RNA (52671 features, 0 variable features)
In [59]:
brain<-brain.test;rm(brain.test)
VlnPlot(brain, features=c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol=3, pt.size=0.5)
In [60]:
bc<-matrix(ncol=4, byrow = T, unlist(strsplit(as.character(colnames(brain@assays$RNA@data)),split=":")))
table(bc[,4])
01 02 03 04 05 06 07 08 09 10 11 12 13 9234 10475 4183 5134 1404 1520 2302 4458 3304 2828 900 2522 11854 14 15 16 17 18 19 20 21 22 23 24 12359 10047 10778 3367 3949 3416 2740 9406 7709 4438 4605
In [61]:
table(bc[,1])
K01 K02 K03 K04 K05 K06 K07 K08 K09 K10 K11 K12 K13 K14 K15 K16 2748 2432 2411 2426 2302 2149 1646 2036 1883 1953 1290 2088 1916 1862 1645 1627 K17 K18 K19 K20 K21 K22 K23 K24 K25 K26 K27 K28 K29 K30 K31 K32 1072 2195 2245 2197 1800 2059 1613 2182 2080 2086 2028 2273 1944 2176 2047 2216 K33 K34 K35 K36 K37 K38 K39 K40 K41 K42 K43 K44 K45 K46 K47 K48 2259 2121 2105 2332 2179 1958 2185 2003 1729 2155 2292 2154 2122 2236 2082 2174 K49 K50 K51 K52 K53 K54 K55 K56 K57 K58 K59 K60 K61 K62 K63 K64 2006 2152 1957 1739 1897 2158 2002 1714 1901 1802 1857 2200 2106 2112 2113 2165 K65 2338
In [62]:
brainregion <- rep("ERC",dim(bc)[1])
brainregion[bc[,4]=="03"|bc[,4]=="04"|bc[,4]=="15"|bc[,4]=="16"]<-"AMY"
brainregion[bc[,4]=="05"|bc[,4]=="06"|bc[,4]=="17"|bc[,4]=="18"]<-"NAC"
brainregion[bc[,4]=="07"|bc[,4]=="08"|bc[,4]=="19"|bc[,4]=="20"]<-"VTA_SnR"
brainregion[bc[,4]=="09"|bc[,4]=="10"|bc[,4]=="21"|bc[,4]=="22"]<-"PFC"
brainregion[bc[,4]=="11"|bc[,4]=="23"]<-"NAC"
brainregion[bc[,4]=="12"|bc[,4]=="24"]<-"PFC"
table(brainregion)
brainregion
AMY ERC NAC PFC VTA_SnR
30142 43922 15578 30374 12916
In [63]:
modality <- rep("H3K27ac",dim(bc)[1])
modality[bc[,4]=="13"|bc[,4]=="14"|bc[,4]=="15"|bc[,4]=="26"|bc[,4]=="17"|bc[,4]=="18"|bc[,4]=="19"|bc[,4]=="20"|bc[,4]=="21"|bc[,4]=="22"|bc[,4]=="23"|bc[,4]=="24"]<-"H3K27me3"
table(modality)
modality H3K27ac H3K27me3 59042 73890
In [64]:
brain@meta.data<-cbind(brain@meta.data, cbind(brainregion, modality))
oriBarcode<-bc[,4]
table(bc[,1])
sublib <- bc[,1]
brain@meta.data<-cbind(brain@meta.data, cbind(oriBarcode,sublib))
brain.exp11.rna <- brain
rm(brain.data,brain,bc,plot1,plot2,brainregion,modality,oriBarcode,sublib)
K01 K02 K03 K04 K05 K06 K07 K08 K09 K10 K11 K12 K13 K14 K15 K16 2748 2432 2411 2426 2302 2149 1646 2036 1883 1953 1290 2088 1916 1862 1645 1627 K17 K18 K19 K20 K21 K22 K23 K24 K25 K26 K27 K28 K29 K30 K31 K32 1072 2195 2245 2197 1800 2059 1613 2182 2080 2086 2028 2273 1944 2176 2047 2216 K33 K34 K35 K36 K37 K38 K39 K40 K41 K42 K43 K44 K45 K46 K47 K48 2259 2121 2105 2332 2179 1958 2185 2003 1729 2155 2292 2154 2122 2236 2082 2174 K49 K50 K51 K52 K53 K54 K55 K56 K57 K58 K59 K60 K61 K62 K63 K64 2006 2152 1957 1739 1897 2158 2002 1714 1901 1802 1857 2200 2106 2112 2113 2165 K65 2338
In [65]:
ls()
- 'brain.exp10.rna'
- 'brain.exp11.rna'
In [66]:
saveRDS(brain.exp11.rna, file = "/projects/ps-renlab2/zhw063/99.MouseBrainPairedTag/11.MouseBrainExp11/merge_mtx/brain.exp11.rna.object.rds")
Experiment 12¶
/projects/ps-renlab2/zhw063/99.MouseBrainPairedTag/12.MouseBrainExp12/merge_mtx
In [68]:
brain.data <- Read10X(data.dir = "/projects/ps-renlab2/zhw063/99.MouseBrainPairedTag/12.MouseBrainExp12/merge_mtx/MouseBrainExp12_RNA")
In [69]:
brain <- CreateSeuratObject(counts = brain.data, project = "mousebrain", min.cells = 0, min.features = 0)
brain[["percent.mt"]]<-PercentageFeatureSet(brain, pattern="^mt-")
options(repr.plot.width=16, repr.plot.height=10)
VlnPlot(brain, features=c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol=3, pt.size=0.5)
In [70]:
brain
An object of class Seurat 52455 features across 134226 samples within 1 assay Active assay: RNA (52455 features, 0 variable features)
In [71]:
summary(brain@meta.data$nFeature_RNA)
Min. 1st Qu. Median Mean 3rd Qu. Max.
233 818 1063 1235 1426 8390
In [72]:
summary(brain@meta.data$nCount_RNA)
Min. 1st Qu. Median Mean 3rd Qu. Max.
800 1214 1701 2165 2490 29996
In [73]:
summary(brain@meta.data$percent.mt)
Min. 1st Qu. Median Mean 3rd Qu. Max. 0.00000 0.00000 0.00000 0.08914 0.07619 11.66566
In [74]:
plot1 <- FeatureScatter(brain, feature1 = "nCount_RNA", feature2 = "percent.mt")
plot2 <- FeatureScatter(brain, feature1 = "nCount_RNA", feature2 = "nFeature_RNA")
plot1 + plot2
Rasterizing points since number of points exceeds 100,000. To disable this behavior set `raster=FALSE` Rasterizing points since number of points exceeds 100,000. To disable this behavior set `raster=FALSE`
In [75]:
brain.test <- subset(brain, subset = nFeature_RNA > 500 & nFeature_RNA < 6000 & percent.mt < 2 & nCount_RNA < 18000)
brain
An object of class Seurat 52455 features across 134226 samples within 1 assay Active assay: RNA (52455 features, 0 variable features)
In [76]:
brain.test
An object of class Seurat 52455 features across 133374 samples within 1 assay Active assay: RNA (52455 features, 0 variable features)
In [77]:
brain<-brain.test;rm(brain.test)
VlnPlot(brain, features=c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol=3, pt.size=0.5)
In [78]:
bc<-matrix(ncol=4, byrow = T, unlist(strsplit(as.character(colnames(brain@assays$RNA@data)),split=":")))
table(bc[,4])
01 02 03 04 05 06 07 08 09 10 11 12 13 10772 11579 5920 6289 3527 4311 7529 10396 1390 694 813 789 12156 14 15 16 17 18 19 20 21 22 23 24 12578 8027 7648 3656 5274 9682 7994 789 770 367 424
In [79]:
table(bc[,1])
L01 L02 L03 L04 L05 L06 L07 L08 L09 L10 L11 L12 L13 L14 L15 L16 2799 2794 2486 2481 2515 2505 2761 2964 2338 2587 2090 2449 2017 2811 2537 2603 L17 L18 L19 L20 L21 L22 L23 L24 L25 L26 L27 L28 L29 L30 L31 L32 2515 2655 2030 2552 2008 1264 2519 2806 1134 2546 1867 1997 1659 2353 2429 2866 L33 L34 L35 L36 L37 L38 L39 L40 L41 L42 L43 L44 L45 L46 L47 L48 2404 2740 2643 2259 2800 2814 2089 2850 2812 2644 2851 2458 2126 2568 2635 1968 L49 L50 L51 L52 L53 L54 2358 2897 2883 2985 2944 2709
In [80]:
brainregion <- rep("CPU",dim(bc)[1])
brainregion[bc[,4]=="03"|bc[,4]=="04"|bc[,4]=="15"|bc[,4]=="16"]<-"HYP"
brainregion[bc[,4]=="05"|bc[,4]=="06"|bc[,4]=="17"|bc[,4]=="18"]<-"HCa"
brainregion[bc[,4]=="07"|bc[,4]=="08"|bc[,4]=="19"|bc[,4]=="20"]<-"HCp"
brainregion[bc[,4]=="09"|bc[,4]=="10"|bc[,4]=="21"|bc[,4]=="22"]<-"PFC"
brainregion[bc[,4]=="11"|bc[,4]=="12"|bc[,4]=="23"|bc[,4]=="24"]<-"PFC"
table(brainregion)
brainregion CPU HCa HCp HYP PFC 47085 16768 35601 27884 6036
In [81]:
modality <- rep("H3K4me1",dim(bc)[1])
modality[bc[,4]=="13"|bc[,4]=="14"|bc[,4]=="15"|bc[,4]=="26"|bc[,4]=="17"|bc[,4]=="18"|bc[,4]=="19"|bc[,4]=="20"|bc[,4]=="21"|bc[,4]=="22"|bc[,4]=="23"|bc[,4]=="24"]<-"H3K9me3"
table(modality)
modality H3K4me1 H3K9me3 71657 61717
In [82]:
brain@meta.data<-cbind(brain@meta.data, cbind(brainregion, modality))
oriBarcode<-bc[,4]
table(bc[,1])
sublib <- bc[,1]
brain@meta.data<-cbind(brain@meta.data, cbind(oriBarcode,sublib))
brain.exp12.rna <- brain
rm(brain.data,brain,bc,plot1,plot2,brainregion,modality,oriBarcode,sublib)
L01 L02 L03 L04 L05 L06 L07 L08 L09 L10 L11 L12 L13 L14 L15 L16 2799 2794 2486 2481 2515 2505 2761 2964 2338 2587 2090 2449 2017 2811 2537 2603 L17 L18 L19 L20 L21 L22 L23 L24 L25 L26 L27 L28 L29 L30 L31 L32 2515 2655 2030 2552 2008 1264 2519 2806 1134 2546 1867 1997 1659 2353 2429 2866 L33 L34 L35 L36 L37 L38 L39 L40 L41 L42 L43 L44 L45 L46 L47 L48 2404 2740 2643 2259 2800 2814 2089 2850 2812 2644 2851 2458 2126 2568 2635 1968 L49 L50 L51 L52 L53 L54 2358 2897 2883 2985 2944 2709
In [83]:
ls()
- 'brain.exp10.rna'
- 'brain.exp11.rna'
- 'brain.exp12.rna'
In [84]:
saveRDS(brain.exp12.rna, file = "/projects/ps-renlab2/zhw063/99.MouseBrainPairedTag/12.MouseBrainExp12/merge_mtx/brain.exp12.rna.object.rds")
Experiment 13¶
/projects/ps-renlab2/zhw063/99.MouseBrainPairedTag/13.MouseBrainExp13/merge_mtx
In [85]:
brain.data <- Read10X(data.dir = "/projects/ps-renlab2/zhw063/99.MouseBrainPairedTag/13.MouseBrainExp13/merge_mtx/MouseBrainExp13_RNA")
In [86]:
brain <- CreateSeuratObject(counts = brain.data, project = "mousebrain", min.cells = 0, min.features = 0)
brain[["percent.mt"]]<-PercentageFeatureSet(brain, pattern="^mt-")
options(repr.plot.width=16, repr.plot.height=10)
VlnPlot(brain, features=c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol=3, pt.size=0.5)
In [87]:
brain
An object of class Seurat 52512 features across 120342 samples within 1 assay Active assay: RNA (52512 features, 0 variable features)
In [88]:
summary(brain@meta.data$nFeature_RNA)
Min. 1st Qu. Median Mean 3rd Qu. Max.
276 800 1054 1234 1439 7670
In [89]:
summary(brain@meta.data$nCount_RNA)
Min. 1st Qu. Median Mean 3rd Qu. Max.
800 1170 1668 2188 2527 29403
In [90]:
summary(brain@meta.data$percent.mt)
Min. 1st Qu. Median Mean 3rd Qu. Max. 0.00000 0.00000 0.00000 0.09725 0.08549 12.24276
In [91]:
plot1 <- FeatureScatter(brain, feature1 = "nCount_RNA", feature2 = "percent.mt")
plot2 <- FeatureScatter(brain, feature1 = "nCount_RNA", feature2 = "nFeature_RNA")
plot1 + plot2
Rasterizing points since number of points exceeds 100,000. To disable this behavior set `raster=FALSE` Rasterizing points since number of points exceeds 100,000. To disable this behavior set `raster=FALSE`
In [92]:
brain.test <- subset(brain, subset = nFeature_RNA > 500 & nFeature_RNA < 6000 & percent.mt < 2 & nCount_RNA < 20000)
brain
An object of class Seurat 52512 features across 120342 samples within 1 assay Active assay: RNA (52512 features, 0 variable features)
In [93]:
brain.test
An object of class Seurat 52512 features across 119644 samples within 1 assay Active assay: RNA (52512 features, 0 variable features)
In [94]:
brain<-brain.test;rm(brain.test)
VlnPlot(brain, features=c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol=3, pt.size=0.5)
In [95]:
bc<-matrix(ncol=4, byrow = T, unlist(strsplit(as.character(colnames(brain@assays$RNA@data)),split=":")))
table(bc[,4])
01 02 03 04 05 06 07 08 09 10 11 12 13 1860 1511 2472 2717 4810 6075 6227 8602 4377 5036 1430 1708 6930 14 15 16 17 18 19 20 21 22 23 12814 8189 5452 9087 12146 4454 3429 4516 5801 1
In [96]:
table(bc[,1])
M01 M02 M03 M04 M05 M06 M07 M08 M09 M10 M11 M12 M13 M14 M15 M16 2376 2555 2641 2752 2320 2121 2725 2537 2490 2302 2572 2583 2447 2889 2667 2567 M17 M18 M19 M20 M21 M22 M23 M24 M25 M26 M27 M28 M29 M30 M31 M32 2579 2510 2718 2713 2032 2703 2695 2401 2489 2406 2931 1750 2324 2790 2535 1989 M33 M34 M35 M36 M37 M38 M39 M40 M41 M42 M43 M44 M45 M46 M47 M48 2276 2271 2606 1997 2348 2526 1667 2199 2313 1615 1823 1067 2425 2187 1271 600 M49 M50 M51 M52 M53 M54 1923 866 1493 1418 1886 758
In [97]:
brainregion <- rep("PFC",dim(bc)[1])
brainregion[bc[,4]=="05"|bc[,4]=="06"|bc[,4]=="17"|bc[,4]=="18"]<-"HCa"
brainregion[bc[,4]=="07"|bc[,4]=="08"]<-"HCp"
brainregion[bc[,4]=="09"|bc[,4]=="10"|bc[,4]=="19"|bc[,4]=="20"]<-"VTA_SnR"
brainregion[bc[,4]=="11"|bc[,4]=="12"|bc[,4]=="21"|bc[,4]=="22"]<-"PFC"
brainregion[bc[,4]=="23"]<-"remove"
table(brainregion)
brainregion
HCa HCp PFC remove VTA_SnR
32118 14829 55400 1 17296
In [98]:
modality <- rep("H3K27ac",dim(bc)[1])
modality[bc[,4]=="13"|bc[,4]=="14"|bc[,4]=="15"|bc[,4]=="16"|bc[,4]=="17"|bc[,4]=="18"|bc[,4]=="19"|bc[,4]=="20"|bc[,4]=="21"|bc[,4]=="22"]<-"H3K27me3"
table(modality)
modality H3K27ac H3K27me3 52278 67366
In [99]:
brain@meta.data<-cbind(brain@meta.data, cbind(brainregion, modality))
oriBarcode<-bc[,4]
table(bc[,1])
sublib <- bc[,1]
brain@meta.data<-cbind(brain@meta.data, cbind(oriBarcode,sublib))
brain.exp13.rna <- brain
rm(brain.data,brain,bc,plot1,plot2,brainregion,modality,oriBarcode,sublib)
M01 M02 M03 M04 M05 M06 M07 M08 M09 M10 M11 M12 M13 M14 M15 M16 2376 2555 2641 2752 2320 2121 2725 2537 2490 2302 2572 2583 2447 2889 2667 2567 M17 M18 M19 M20 M21 M22 M23 M24 M25 M26 M27 M28 M29 M30 M31 M32 2579 2510 2718 2713 2032 2703 2695 2401 2489 2406 2931 1750 2324 2790 2535 1989 M33 M34 M35 M36 M37 M38 M39 M40 M41 M42 M43 M44 M45 M46 M47 M48 2276 2271 2606 1997 2348 2526 1667 2199 2313 1615 1823 1067 2425 2187 1271 600 M49 M50 M51 M52 M53 M54 1923 866 1493 1418 1886 758
In [7]:
ls()
In [101]:
saveRDS(brain.exp13.rna, file = "/projects/ps-renlab2/zhw063/99.MouseBrainPairedTag/13.MouseBrainExp13/merge_mtx/brain.exp13.rna.object.rds")
Experiment 14¶
/projects/ps-renlab2/zhw063/99.MouseBrainPairedTag/14.MouseBrainExp14/merge_mtx
In [7]:
brain.data <- Read10X(data.dir = "/projects/ps-renlab2/zhw063/99.MouseBrainPairedTag/14.MouseBrainExp14/merge_mtx/MouseBrainExp14_RNA_new")
In [8]:
brain <- CreateSeuratObject(counts = brain.data, project = "mousebrain", min.cells = 0, min.features = 0)
brain[["percent.mt"]]<-PercentageFeatureSet(brain, pattern="^mt-")
options(repr.plot.width=16, repr.plot.height=10)
VlnPlot(brain, features=c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol=3, pt.size=0.5)
In [9]:
brain
An object of class Seurat 52888 features across 97694 samples within 1 assay Active assay: RNA (52888 features, 0 variable features)
In [10]:
summary(brain@meta.data$nFeature_RNA)
Min. 1st Qu. Median Mean 3rd Qu. Max.
326 781 1049 1250 1488 8325
In [11]:
summary(brain@meta.data$nCount_RNA)
Min. 1st Qu. Median Mean 3rd Qu. Max.
500 1128 1634 2199 2585 29738
In [12]:
summary(brain@meta.data$percent.mt)
Min. 1st Qu. Median Mean 3rd Qu. Max. 0.0000 0.0000 0.0000 0.1522 0.1938 12.3211
In [13]:
plot1 <- FeatureScatter(brain, feature1 = "nCount_RNA", feature2 = "percent.mt")
plot2 <- FeatureScatter(brain, feature1 = "nCount_RNA", feature2 = "nFeature_RNA")
plot1 + plot2
In [14]:
brain.test <- subset(brain, subset = nFeature_RNA > 500 & nFeature_RNA < 6000 & percent.mt < 2 & nCount_RNA < 20000)
brain
An object of class Seurat 52888 features across 97694 samples within 1 assay Active assay: RNA (52888 features, 0 variable features)
In [15]:
brain.test
An object of class Seurat 52888 features across 96677 samples within 1 assay Active assay: RNA (52888 features, 0 variable features)
In [17]:
brain<-brain.test;rm(brain.test)
VlnPlot(brain, features=c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol=3, pt.size=0.5)
In [18]:
bc<-matrix(ncol=4, byrow = T, unlist(strsplit(as.character(colnames(brain@assays$RNA@data)),split=":")))
table(bc[,4])
01 02 03 04 05 06 07 08 09 10 11 12 13 14 15 16 1286 1726 4797 3008 2443 3354 2532 3720 3697 3785 5640 4732 4340 3130 3979 3305 17 18 19 20 21 22 23 24 5283 5916 3486 2758 6647 6528 4686 5899
In [19]:
table(bc[,1])
N01 N02 N03 N04 N05 N06 N07 N08 N09 N10 N11 N12 N13 N14 N15 N16 1735 1682 1493 1334 1566 1870 1693 1544 1620 1608 1418 1431 1591 1593 1483 1460 N17 N18 N19 N20 N21 N22 N23 N24 N25 N26 N27 N28 N29 N30 N31 N32 1340 1470 1612 1497 1691 1543 1683 1693 1749 1149 1097 1001 1204 1215 1262 1343 N33 N34 N35 N36 N37 N38 N39 N40 N41 N42 N43 N44 N45 N46 N47 N48 1203 1144 1413 1480 1347 1475 1614 1557 1428 1244 1065 1263 1710 1672 1612 1041 N49 N50 N51 N52 N53 N54 N55 N56 N57 N58 N59 N60 N61 N62 1648 1774 1879 1917 2110 1563 1652 1936 1695 2102 2126 2207 2039 2091
In [20]:
brainregion <- rep("PFC",dim(bc)[1])
brainregion[bc[,4]=="03"|bc[,4]=="04"|bc[,4]=="11"|bc[,4]=="12"]<-"HCa"
brainregion[bc[,4]=="05"|bc[,4]=="06"|bc[,4]=="15"|bc[,4]=="16"]<-"ERC"
brainregion[bc[,4]=="07"|bc[,4]=="08"]<-"HYP"
brainregion[bc[,4]=="09"|bc[,4]=="10"|bc[,4]=="19"|bc[,4]=="20"]<-"VTA_SnR"
brainregion[bc[,4]=="17"|bc[,4]=="18"]<-"AMY"
brainregion[bc[,4]=="21"|bc[,4]=="22"]<-"HCa"
table(brainregion)
brainregion
AMY ERC HCa HYP PFC VTA_SnR
11199 13081 31352 6252 21067 13726
In [21]:
modality <- rep("H3K4me1",dim(bc)[1])
modality[bc[,4]=="13"|bc[,4]=="14"|bc[,4]=="15"|bc[,4]=="16"|bc[,4]=="17"|bc[,4]=="18"|bc[,4]=="19"|bc[,4]=="20"|bc[,4]=="21"|bc[,4]=="22"|bc[,4]=="23"|bc[,4]=="24"]<-"H3K9me3"
table(modality)
modality H3K4me1 H3K9me3 44025 52652
In [22]:
brain@meta.data<-cbind(brain@meta.data, cbind(brainregion, modality))
oriBarcode<-bc[,4]
table(bc[,1])
sublib <- bc[,1]
brain@meta.data<-cbind(brain@meta.data, cbind(oriBarcode,sublib))
brain.exp14.rna <- brain
rm(brain.data,brain,bc,plot1,plot2,brainregion,modality,oriBarcode,sublib)
N01 N02 N03 N04 N05 N06 N07 N08 N09 N10 N11 N12 N13 N14 N15 N16 1735 1682 1493 1334 1566 1870 1693 1544 1620 1608 1418 1431 1591 1593 1483 1460 N17 N18 N19 N20 N21 N22 N23 N24 N25 N26 N27 N28 N29 N30 N31 N32 1340 1470 1612 1497 1691 1543 1683 1693 1749 1149 1097 1001 1204 1215 1262 1343 N33 N34 N35 N36 N37 N38 N39 N40 N41 N42 N43 N44 N45 N46 N47 N48 1203 1144 1413 1480 1347 1475 1614 1557 1428 1244 1065 1263 1710 1672 1612 1041 N49 N50 N51 N52 N53 N54 N55 N56 N57 N58 N59 N60 N61 N62 1648 1774 1879 1917 2110 1563 1652 1936 1695 2102 2126 2207 2039 2091
In [23]:
ls()
'brain.exp14.rna'
In [24]:
saveRDS(brain.exp14.rna, file = "/projects/ps-renlab2/zhw063/99.MouseBrainPairedTag/14.MouseBrainExp14/merge_mtx/brain.exp14.rna.object.rds")
Merge all RNA objects¶
In [11]:
library(sctransform)
In [8]:
sessionInfo()
R version 4.2.3 (2023-03-15) Platform: x86_64-conda-linux-gnu (64-bit) Running under: CentOS Linux 7 (Core) Matrix products: default BLAS/LAPACK: /projects/ps-renlab2/zhw063/miniconda3/envs/singlecell2/lib/libopenblasp-r0.3.21.so locale: [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8 [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8 [7] LC_PAPER=en_US.UTF-8 LC_NAME=C [9] LC_ADDRESS=C LC_TELEPHONE=C [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C attached base packages: [1] stats4 stats graphics grDevices utils datasets methods [8] base other attached packages: [1] tibble_3.2.1 EnsDb.Mmusculus.v79_2.99.0 [3] ensembldb_2.22.0 AnnotationFilter_1.22.0 [5] GenomicFeatures_1.50.2 AnnotationDbi_1.60.0 [7] Biobase_2.58.0 GenomicRanges_1.50.0 [9] GenomeInfoDb_1.34.8 IRanges_2.32.0 [11] harmony_1.0 Rcpp_1.0.10 [13] pheatmap_1.0.12 ggseqlogo_0.1 [15] ggforce_0.4.1 S4Vectors_0.36.0 [17] BiocGenerics_0.44.0 RColorBrewer_1.1-3 [19] dplyr_1.1.1 cowplot_1.1.1 [21] ggridges_0.5.4 ggrepel_0.9.3 [23] ggplot2_3.4.2 Signac_1.9.0 [25] SeuratObject_4.1.3 Seurat_4.3.0 [27] sctransform_0.3.5 loaded via a namespace (and not attached): [1] utf8_1.2.3 spatstat.explore_3.1-0 [3] reticulate_1.25 tidyselect_1.2.0 [5] RSQLite_2.3.1 htmlwidgets_1.6.2 [7] grid_4.2.3 BiocParallel_1.32.5 [9] Rtsne_0.16 munsell_0.5.0 [11] codetools_0.2-19 ica_1.0-3 [13] pbdZMQ_0.3-9 future_1.32.0 [15] miniUI_0.1.1.1 withr_2.5.0 [17] spatstat.random_3.1-4 colorspace_2.1-0 [19] progressr_0.13.0 filelock_1.0.2 [21] uuid_1.1-0 ROCR_1.0-11 [23] tensor_1.5 listenv_0.9.0 [25] MatrixGenerics_1.10.0 repr_1.1.6 [27] GenomeInfoDbData_1.2.9 polyclip_1.10-4 [29] bit64_4.0.5 farver_2.1.1 [31] parallelly_1.35.0 vctrs_0.6.1 [33] generics_0.1.3 BiocFileCache_2.6.0 [35] R6_2.5.1 bitops_1.0-7 [37] spatstat.utils_3.0-2 cachem_1.0.7 [39] DelayedArray_0.24.0 promises_1.2.0.1 [41] BiocIO_1.8.0 scales_1.2.1 [43] gtable_0.3.3 globals_0.16.2 [45] goftest_1.2-3 rlang_1.1.0 [47] RcppRoll_0.3.0 splines_4.2.3 [49] rtracklayer_1.58.0 lazyeval_0.2.2 [51] spatstat.geom_3.1-0 yaml_2.3.7 [53] reshape2_1.4.4 abind_1.4-5 [55] httpuv_1.6.9 tools_4.2.3 [57] ellipsis_0.3.2 plyr_1.8.8 [59] base64enc_0.1-3 progress_1.2.2 [61] zlibbioc_1.44.0 purrr_1.0.1 [63] RCurl_1.98-1.12 prettyunits_1.1.1 [65] deldir_1.0-6 pbapply_1.7-0 [67] zoo_1.8-12 SummarizedExperiment_1.28.0 [69] cluster_2.1.4 magrittr_2.0.3 [71] data.table_1.14.8 scattermore_0.8 [73] lmtest_0.9-40 RANN_2.6.1 [75] ProtGenerics_1.30.0 fitdistrplus_1.1-8 [77] matrixStats_0.63.0 hms_1.1.3 [79] patchwork_1.1.2 mime_0.12 [81] evaluate_0.20 xtable_1.8-4 [83] XML_3.99-0.14 gridExtra_2.3 [85] compiler_4.2.3 biomaRt_2.54.0 [87] KernSmooth_2.23-20 crayon_1.5.2 [89] htmltools_0.5.5 later_1.3.0 [91] tidyr_1.3.0 DBI_1.1.3 [93] tweenr_2.0.2 dbplyr_2.3.2 [95] MASS_7.3-58.3 rappdirs_0.3.3 [97] Matrix_1.5-4 cli_3.6.1 [99] parallel_4.2.3 igraph_1.4.2 [101] pkgconfig_2.0.3 GenomicAlignments_1.34.0 [103] sp_1.6-0 IRdisplay_1.1 [105] plotly_4.10.1 spatstat.sparse_3.0-1 [107] xml2_1.3.3 XVector_0.38.0 [109] stringr_1.5.0 digest_0.6.31 [111] RcppAnnoy_0.0.20 spatstat.data_3.0-1 [113] Biostrings_2.66.0 leiden_0.4.3 [115] fastmatch_1.1-3 uwot_0.1.14 [117] restfulr_0.0.15 curl_4.3.3 [119] shiny_1.7.4 Rsamtools_2.14.0 [121] rjson_0.2.21 lifecycle_1.0.3 [123] nlme_3.1-162 jsonlite_1.8.4 [125] viridisLite_0.4.1 fansi_1.0.4 [127] pillar_1.9.0 lattice_0.21-8 [129] KEGGREST_1.38.0 fastmap_1.1.1 [131] httr_1.4.5 survival_3.5-5 [133] glue_1.6.2 png_0.1-8 [135] bit_4.0.5 stringi_1.7.12 [137] blob_1.2.4 memoise_2.0.1 [139] IRkernel_1.3.2 irlba_2.3.5.1 [141] future.apply_1.10.0
In [12]:
brain.exp1.rna <- readRDS(file = "./01.MouseBrainExp1/merge_mtx/brain.exp1.rna.object.rds")
brain.exp2.rna <- readRDS(file = "./02.MouseBrainExp2/merge_mtx/brain.exp2.rna.object.rds")
brain.exp3.rna <- readRDS(file = "./03.MouseBrainExp3/merge_mtx/brain.exp3.rna.object.rds")
brain.exp4.rna <- readRDS(file = "./04.MouseBrainExp4/merge_mtx/brain.exp4.rna.object.rds")
brain.exp5.rna <- readRDS(file = "./05.MouseBrainExp5/merge_mtx/brain.exp5.rna.object.rds")
brain.exp6.rna <- readRDS(file = "./06.MouseBrainExp6/merge_mtx/brain.exp6.rna.object.rds")
brain.exp7.rna <- readRDS(file = "./07.MouseBrainExp7/merge_mtx/brain.exp7.rna.object.rds")
brain.exp8.rna <- readRDS(file = "./08.MouseBrainExp8/merge_mtx/brain.exp8.rna.object.rds")
brain.exp9.rna <- readRDS(file = "./09.MouseBrainExp9/merge_mtx/brain.exp9.rna.object.rds")
brain.exp10.rna <- readRDS(file = "./10.MouseBrainExp10/merge_mtx/brain.exp10.rna.object.rds")
brain.exp11.rna <- readRDS(file = "./11.MouseBrainExp11/merge_mtx/brain.exp11.rna.object.rds")
brain.exp12.rna <- readRDS(file = "./12.MouseBrainExp12/merge_mtx/brain.exp12.rna.object.rds")
brain.exp13.rna <- readRDS(file = "./13.MouseBrainExp13/merge_mtx/brain.exp13.rna.object.rds")
brain.exp14.rna <- readRDS(file = "./14.MouseBrainExp14/merge_mtx/brain.exp14.rna.object.rds")
In [13]:
ls()
- 'brain.exp1.rna'
- 'brain.exp10.rna'
- 'brain.exp11.rna'
- 'brain.exp12.rna'
- 'brain.exp13.rna'
- 'brain.exp14.rna'
- 'brain.exp2.rna'
- 'brain.exp3.rna'
- 'brain.exp4.rna'
- 'brain.exp5.rna'
- 'brain.exp6.rna'
- 'brain.exp7.rna'
- 'brain.exp8.rna'
- 'brain.exp9.rna'
In [15]:
options(Seurat.object.assay.version = 'v5')
options(future.globals.maxSize = 6e9)
In [16]:
brain.exp1.rna.v5<-UpdateSeuratObject(brain.exp1.rna)
Validating object structure Updating object slots Ensuring keys are in the proper structure Ensuring keys are in the proper structure Ensuring feature names don't have underscores or pipes Updating slots in RNA Validating object structure for Assay ‘RNA’ Object representation is consistent with the most current Seurat version
In [18]:
brain.exp2.rna.v5<-UpdateSeuratObject(brain.exp2.rna)
brain.exp3.rna.v5<-UpdateSeuratObject(brain.exp3.rna)
brain.exp4.rna.v5<-UpdateSeuratObject(brain.exp4.rna)
brain.exp5.rna.v5<-UpdateSeuratObject(brain.exp5.rna)
brain.exp6.rna.v5<-UpdateSeuratObject(brain.exp6.rna)
brain.exp7.rna.v5<-UpdateSeuratObject(brain.exp7.rna)
brain.exp8.rna.v5<-UpdateSeuratObject(brain.exp8.rna)
brain.exp9.rna.v5<-UpdateSeuratObject(brain.exp9.rna)
brain.exp10.rna.v5<-UpdateSeuratObject(brain.exp10.rna)
brain.exp11.rna.v5<-UpdateSeuratObject(brain.exp11.rna)
brain.exp12.rna.v5<-UpdateSeuratObject(brain.exp12.rna)
brain.exp13.rna.v5<-UpdateSeuratObject(brain.exp13.rna)
brain.exp14.rna.v5<-UpdateSeuratObject(brain.exp14.rna)
Validating object structure Updating object slots Ensuring keys are in the proper structure Ensuring keys are in the proper structure Ensuring feature names don't have underscores or pipes Updating slots in RNA Validating object structure for Assay ‘RNA’ Object representation is consistent with the most current Seurat version Validating object structure Updating object slots Ensuring keys are in the proper structure Ensuring keys are in the proper structure Ensuring feature names don't have underscores or pipes Updating slots in RNA Validating object structure for Assay ‘RNA’ Object representation is consistent with the most current Seurat version Validating object structure Updating object slots Ensuring keys are in the proper structure Ensuring keys are in the proper structure Ensuring feature names don't have underscores or pipes Updating slots in RNA Validating object structure for Assay ‘RNA’ Object representation is consistent with the most current Seurat version Validating object structure Updating object slots Ensuring keys are in the proper structure Ensuring keys are in the proper structure Ensuring feature names don't have underscores or pipes Updating slots in RNA Validating object structure for Assay ‘RNA’ Object representation is consistent with the most current Seurat version Validating object structure Updating object slots Ensuring keys are in the proper structure Ensuring keys are in the proper structure Ensuring feature names don't have underscores or pipes Updating slots in RNA Validating object structure for Assay ‘RNA’ Object representation is consistent with the most current Seurat version Validating object structure Updating object slots Ensuring keys are in the proper structure Ensuring keys are in the proper structure Ensuring feature names don't have underscores or pipes Updating slots in RNA Validating object structure for Assay ‘RNA’ Object representation is consistent with the most current Seurat version Validating object structure Updating object slots Ensuring keys are in the proper structure Ensuring keys are in the proper structure Ensuring feature names don't have underscores or pipes Updating slots in RNA Validating object structure for Assay ‘RNA’ Object representation is consistent with the most current Seurat version Validating object structure Updating object slots Ensuring keys are in the proper structure Ensuring keys are in the proper structure Ensuring feature names don't have underscores or pipes Updating slots in RNA Validating object structure for Assay ‘RNA’ Object representation is consistent with the most current Seurat version Validating object structure Updating object slots Ensuring keys are in the proper structure Ensuring keys are in the proper structure Ensuring feature names don't have underscores or pipes Updating slots in RNA Validating object structure for Assay ‘RNA’ Object representation is consistent with the most current Seurat version Validating object structure Updating object slots Ensuring keys are in the proper structure Ensuring keys are in the proper structure Ensuring feature names don't have underscores or pipes Updating slots in RNA Validating object structure for Assay ‘RNA’ Object representation is consistent with the most current Seurat version Validating object structure Updating object slots Ensuring keys are in the proper structure Ensuring keys are in the proper structure Ensuring feature names don't have underscores or pipes Updating slots in RNA Validating object structure for Assay ‘RNA’ Object representation is consistent with the most current Seurat version Validating object structure Updating object slots Ensuring keys are in the proper structure Ensuring keys are in the proper structure Ensuring feature names don't have underscores or pipes Updating slots in RNA Validating object structure for Assay ‘RNA’ Object representation is consistent with the most current Seurat version Validating object structure Updating object slots Ensuring keys are in the proper structure Ensuring keys are in the proper structure Ensuring feature names don't have underscores or pipes Updating slots in RNA Validating object structure for Assay ‘RNA’ Object representation is consistent with the most current Seurat version
In [19]:
ls()
- 'brain.exp1.rna'
- 'brain.exp1.rna.v5'
- 'brain.exp10.rna'
- 'brain.exp10.rna.v5'
- 'brain.exp11.rna'
- 'brain.exp11.rna.v5'
- 'brain.exp12.rna'
- 'brain.exp12.rna.v5'
- 'brain.exp13.rna'
- 'brain.exp13.rna.v5'
- 'brain.exp14.rna'
- 'brain.exp14.rna.v5'
- 'brain.exp2.rna'
- 'brain.exp2.rna.v5'
- 'brain.exp3.rna'
- 'brain.exp3.rna.v5'
- 'brain.exp4.rna'
- 'brain.exp4.rna.v5'
- 'brain.exp5.rna'
- 'brain.exp5.rna.v5'
- 'brain.exp6.rna'
- 'brain.exp6.rna.v5'
- 'brain.exp7.rna'
- 'brain.exp7.rna.v5'
- 'brain.exp8.rna'
- 'brain.exp8.rna.v5'
- 'brain.exp9.rna'
- 'brain.exp9.rna.v5'
In [20]:
rm(brain.exp1.rna, brain.exp2.rna, brain.exp3.rna, brain.exp4.rna, brain.exp5.rna, brain.exp6.rna, brain.exp7.rna,
brain.exp8.rna, brain.exp9.rna, brain.exp10.rna, brain.exp11.rna, brain.exp12.rna, brain.exp13.rna, brain.exp14.rna)
In [21]:
ls()
- 'brain.exp1.rna.v5'
- 'brain.exp10.rna.v5'
- 'brain.exp11.rna.v5'
- 'brain.exp12.rna.v5'
- 'brain.exp13.rna.v5'
- 'brain.exp14.rna.v5'
- 'brain.exp2.rna.v5'
- 'brain.exp3.rna.v5'
- 'brain.exp4.rna.v5'
- 'brain.exp5.rna.v5'
- 'brain.exp6.rna.v5'
- 'brain.exp7.rna.v5'
- 'brain.exp8.rna.v5'
- 'brain.exp9.rna.v5'
In [22]:
brain.exp1.rna.v5[["sex"]] <- "Female"
brain.exp2.rna.v5[["sex"]] <- "Female"
brain.exp3.rna.v5[["sex"]] <- "Female"
brain.exp4.rna.v5[["sex"]] <- "Female"
brain.exp5.rna.v5[["sex"]] <- "Male"
brain.exp6.rna.v5[["sex"]] <- "Male"
brain.exp7.rna.v5[["sex"]] <- "Male"
brain.exp8.rna.v5[["sex"]] <- "Female"
brain.exp9.rna.v5[["sex"]] <- "Female"
brain.exp10.rna.v5[["sex"]] <- "Male"
brain.exp11.rna.v5[["sex"]] <- "Female"
brain.exp12.rna.v5[["sex"]] <- "Female"
brain.exp13.rna.v5[["sex"]] <- "Male"
In [23]:
brain.exp1.rna.v5[["rep"]] <- "FemaleB"
brain.exp2.rna.v5[["rep"]] <- "FemaleB"
brain.exp3.rna.v5[["rep"]] <- "FemaleB"
brain.exp4.rna.v5[["rep"]] <- "FemaleB"
brain.exp5.rna.v5[["rep"]] <- "MaleA"
brain.exp6.rna.v5[["rep"]] <- "MaleA"
brain.exp7.rna.v5[["rep"]] <- "MaleB"
brain.exp8.rna.v5[["rep"]] <- "FemaleA"
brain.exp9.rna.v5[["rep"]] <- "FemaleA"
brain.exp10.rna.v5[["rep"]] <- "MaleB"
brain.exp11.rna.v5[["rep"]] <- "FemaleA"
brain.exp12.rna.v5[["rep"]] <- "FemaleA"
In [24]:
bc<-matrix(ncol=4, byrow = T, unlist(strsplit(as.character(colnames(brain.exp13.rna.v5@assays$RNA@data)),split=":")))
table(bc[,4])
01 02 03 04 05 06 07 08 09 10 11 12 13 1860 1511 2472 2717 4810 6075 6227 8602 4377 5036 1430 1708 6930 14 15 16 17 18 19 20 21 22 23 12814 8189 5452 9087 12146 4454 3429 4516 5801 1
In [25]:
replicate <- rep("MaleA",dim(bc)[1])
replicate[bc[,4]=="03"|bc[,4]=="04"|bc[,4]=="12"|bc[,4]=="15"|bc[,4]=="16"|bc[,4]=="22"]<-"MaleB"
replicate[bc[,4]=="06"|bc[,4]=="08"|bc[,4]=="10"]<-"MaleB"
replicate[bc[,4]=="18"|bc[,4]=="20"]<-"MaleB"
table(replicate)
replicate MaleA MaleB 58017 61627
In [26]:
brain.exp13.rna.v5@meta.data<-cbind(brain.exp13.rna.v5@meta.data, cbind(replicate))
In [27]:
ls()
- 'bc'
- 'brain.exp1.rna.v5'
- 'brain.exp10.rna.v5'
- 'brain.exp11.rna.v5'
- 'brain.exp12.rna.v5'
- 'brain.exp13.rna.v5'
- 'brain.exp14.rna.v5'
- 'brain.exp2.rna.v5'
- 'brain.exp3.rna.v5'
- 'brain.exp4.rna.v5'
- 'brain.exp5.rna.v5'
- 'brain.exp6.rna.v5'
- 'brain.exp7.rna.v5'
- 'brain.exp8.rna.v5'
- 'brain.exp9.rna.v5'
- 'replicate'
In [36]:
table(brain.exp1.rna@meta.data$brainregion,brain.exp1.rna@meta.data$modality)
H3K27ac H3K27me3
CPU 4314 9673
HCa 4223 8256
HCp 2177 5902
HYP 2812 5765
In [37]:
table(brain.exp2.rna@meta.data$brainregion,brain.exp2.rna@meta.data$modality)
H3K4me1 H3K9me3
CPU 14671 15513
HCa 6976 10995
HCp 9550 9008
HYP 7913 10507
In [38]:
table(brain.exp3.rna@meta.data$brainregion,brain.exp3.rna@meta.data$modality)
H3K27ac H3K27me3
AMY 2312 9119
ERC 3194 11305
mESC 9557 1793
NAC 1131 2502
PFC 927 5423
VTA_SnR 3582 5641
In [39]:
table(brain.exp4.rna@meta.data$brainregion,brain.exp4.rna@meta.data$modality)
H3K4me1 H3K9me3
AMY 5939 6267
ERC 7562 8772
NAC 10066 9517
PFC 2333 860
VTA_SnR 6559 7190
In [40]:
table(brain.exp5.rna@meta.data$brainregion,brain.exp5.rna@meta.data$modality)
H3K27ac H3K27me3
AMY 5117 7680
CPU 12033 21265
ERC 8944 12224
HCa 7772 15352
HCp 9541 13395
HYP 9109 14363
ITGremove 8 0
NAC 6048 12099
VTA_SnR 3952 5140
In [41]:
table(brain.exp6.rna@meta.data$brainregion,brain.exp6.rna@meta.data$modality)
H3K4me1 H3K9me3
AMY 3031 1667
CPU 4131 3041
ERC 4686 1888
HCa 2364 3463
HCp 4215 4379
HYP 1994 2677
NAC 7315 2777
VTA_SnR 1840 1072
In [42]:
table(brain.exp7.rna@meta.data$brainregion,brain.exp7.rna@meta.data$modality)
H3K27ac H3K27me3
AMY 3769 8323
CPU 7318 17765
ERC 8987 11710
HCa 2214 3788
HCp 4206 8506
HYP 5010 11086
NAC 3411 3691
VTA_SnR 3665 3864
In [43]:
table(brain.exp8.rna@meta.data$brainregion,brain.exp8.rna@meta.data$modality)
H3K27ac H3K27me3
CPU 10469 21741
HCa 6868 19722
HCp 6017 13973
HYP 7050 13918
In [44]:
table(brain.exp9.rna@meta.data$brainregion,brain.exp9.rna@meta.data$modality)
H3K4me1 H3K9me3
AMY 13227 14400
ERC 17469 15466
NAC 10856 15420
VTA_SnR 7570 7746
In [45]:
table(brain.exp10.rna@meta.data$brainregion,brain.exp10.rna@meta.data$modality)
H3K4me1 H3K9me3
AMY 19934 17938
CPU 24646 25417
ERC 8429 8980
HCa 16622 19715
HCp 16583 16009
HYP 22058 21110
NAC 22724 21051
VTA_SnR 15664 9805
In [46]:
table(brain.exp11.rna@meta.data$brainregion,brain.exp11.rna@meta.data$modality)
H3K27ac H3K27me3
AMY 20095 10047
ERC 19709 24213
NAC 3824 11754
PFC 8654 21720
VTA_SnR 6760 6156
In [47]:
table(brain.exp12.rna@meta.data$brainregion,brain.exp12.rna@meta.data$modality)
H3K4me1 H3K9me3
CPU 22351 24734
HCa 7838 8930
HCp 17925 17676
HYP 19857 8027
PFC 3686 2350
In [50]:
table(brain.exp13.rna@meta.data$oriBarcode,brain.exp13.rna@meta.data$modality)
H3K27ac H3K27me3
01 1860 0
02 1511 0
03 2472 0
04 2717 0
05 4810 0
06 6075 0
07 6227 0
08 8602 0
09 4377 0
10 5036 0
11 1430 0
12 1708 0
13 0 6930
14 0 12814
15 0 8189
16 5452 0
17 0 9087
18 0 12146
19 0 4454
20 0 3429
21 0 4516
22 0 5801
23 1 0
In [28]:
bc<-matrix(ncol=4, byrow = T, unlist(strsplit(as.character(colnames(brain.exp13.rna.v5@assays$RNA@data)),split=":")))
table(bc[,4])
01 02 03 04 05 06 07 08 09 10 11 12 13 1860 1511 2472 2717 4810 6075 6227 8602 4377 5036 1430 1708 6930 14 15 16 17 18 19 20 21 22 23 12814 8189 5452 9087 12146 4454 3429 4516 5801 1
In [29]:
brain.exp13.rna.v5[["modality"]] <- NULL
In [30]:
modality <- rep("H3K27ac",dim(bc)[1])
modality[bc[,4]=="13"|bc[,4]=="14"|bc[,4]=="15"|bc[,4]=="16"|bc[,4]=="17"|bc[,4]=="18"|bc[,4]=="19"|bc[,4]=="20"|bc[,4]=="21"|bc[,4]=="22"]<-"H3K27me3"
table(modality)
modality H3K27ac H3K27me3 46826 72818
In [31]:
brain.exp13.rna.v5@meta.data<-cbind(brain.exp13.rna.v5@meta.data, cbind(modality))
In [32]:
table(brain.exp13.rna.v5@meta.data$oriBarcode,brain.exp13.rna.v5@meta.data$modality)
H3K27ac H3K27me3
01 1860 0
02 1511 0
03 2472 0
04 2717 0
05 4810 0
06 6075 0
07 6227 0
08 8602 0
09 4377 0
10 5036 0
11 1430 0
12 1708 0
13 0 6930
14 0 12814
15 0 8189
16 0 5452
17 0 9087
18 0 12146
19 0 4454
20 0 3429
21 0 4516
22 0 5801
23 1 0
In [33]:
replicate <- rep("MaleA",dim(bc)[1])
replicate[bc[,4]=="03"|bc[,4]=="04"|bc[,4]=="05"|bc[,4]=="06"|bc[,4]=="08"|bc[,4]=="10"|bc[,4]=="12"|bc[,4]=="15"|bc[,4]=="16"|bc[,4]=="17"|bc[,4]=="18"|bc[,4]=="20"|bc[,4]=="22"]<-"MaleB"
table(replicate)
replicate MaleA MaleB 44120 75524
In [34]:
brain.exp13.rna.v5[["replicate"]] <- NULL
In [35]:
brain.exp13.rna.v5@meta.data<-cbind(brain.exp13.rna.v5@meta.data, cbind(replicate))
In [36]:
table(brain.exp13.rna.v5@meta.data$replicate,brain.exp13.rna.v5@meta.data$brainregion)
HCa HCp PFC remove VTA_SnR
MaleA 0 6227 29061 1 8831
MaleB 32118 8602 26339 0 8465
In [27]:
table(brain.exp13.rna.v5@meta.data$brainregion,brain.exp13.rna.v5@meta.data$modality)
H3K27ac H3K27me3
HCa 10885 21233
HCp 14829 0
PFC 11698 43702
remove 1 0
VTA_SnR 9413 7883
In [37]:
table(brain.exp14.rna.v5@meta.data$oriBarcode,brain.exp14.rna.v5@meta.data$modality)
H3K4me1 H3K9me3
01 1286 0
02 1726 0
03 4797 0
04 3008 0
05 2443 0
06 3354 0
07 2532 0
08 3720 0
09 3697 0
10 3785 0
11 5640 0
12 4732 0
13 0 4340
14 0 3130
15 0 3979
16 3305 0
17 0 5283
18 0 5916
19 0 3486
20 0 2758
21 0 6647
22 0 6528
23 0 4686
24 0 5899
In [38]:
bc<-matrix(ncol=4, byrow = T, unlist(strsplit(as.character(colnames(brain.exp14.rna.v5@assays$RNA@data)),split=":")))
table(bc[,4])
01 02 03 04 05 06 07 08 09 10 11 12 13 14 15 16 1286 1726 4797 3008 2443 3354 2532 3720 3697 3785 5640 4732 4340 3130 3979 3305 17 18 19 20 21 22 23 24 5283 5916 3486 2758 6647 6528 4686 5899
In [39]:
brain.exp14.rna.v5[["modality"]] <- NULL
In [40]:
modality <- rep("H3K4me1",dim(bc)[1])
modality[bc[,4]=="13"|bc[,4]=="14"|bc[,4]=="15"|bc[,4]=="16"|bc[,4]=="17"|bc[,4]=="18"|bc[,4]=="19"|bc[,4]=="20"|bc[,4]=="21"|bc[,4]=="22"|bc[,4]=="23"|bc[,4]=="24"]<-"H3K9me3"
table(modality)
modality H3K4me1 H3K9me3 40720 55957
In [41]:
brain.exp14.rna.v5@meta.data<-cbind(brain.exp14.rna.v5@meta.data, cbind(modality))
In [42]:
table(brain.exp14.rna.v5@meta.data$oriBarcode,brain.exp14.rna.v5@meta.data$modality)
H3K4me1 H3K9me3
01 1286 0
02 1726 0
03 4797 0
04 3008 0
05 2443 0
06 3354 0
07 2532 0
08 3720 0
09 3697 0
10 3785 0
11 5640 0
12 4732 0
13 0 4340
14 0 3130
15 0 3979
16 0 3305
17 0 5283
18 0 5916
19 0 3486
20 0 2758
21 0 6647
22 0 6528
23 0 4686
24 0 5899
In [43]:
replicate <- rep("MaleA",dim(bc)[1])
replicate[bc[,4]=="02"|bc[,4]=="14"]<-"MaleB"
replicate[bc[,4]=="11"|bc[,4]=="21"]<-"FemaleA"
replicate[bc[,4]=="12"|bc[,4]=="22"]<-"FemaleB"
table(replicate)
replicate FemaleA FemaleB MaleA MaleB 12287 11260 68274 4856
In [44]:
brain.exp14.rna.v5@meta.data<-cbind(brain.exp14.rna.v5@meta.data, cbind(replicate))
In [45]:
table(brain.exp14.rna.v5@meta.data$oriBarcode,brain.exp14.rna.v5@meta.data$replicate)
FemaleA FemaleB MaleA MaleB
01 0 0 1286 0
02 0 0 0 1726
03 0 0 4797 0
04 0 0 3008 0
05 0 0 2443 0
06 0 0 3354 0
07 0 0 2532 0
08 0 0 3720 0
09 0 0 3697 0
10 0 0 3785 0
11 5640 0 0 0
12 0 4732 0 0
13 0 0 4340 0
14 0 0 0 3130
15 0 0 3979 0
16 0 0 3305 0
17 0 0 5283 0
18 0 0 5916 0
19 0 0 3486 0
20 0 0 2758 0
21 6647 0 0 0
22 0 6528 0 0
23 0 0 4686 0
24 0 0 5899 0
In [46]:
table(brain.exp14.rna.v5@meta.data$brainregion,brain.exp14.rna.v5@meta.data$modality)
H3K4me1 H3K9me3
AMY 0 11199
ERC 5797 7284
HCa 18177 13175
HYP 6252 0
PFC 3012 18055
VTA_SnR 7482 6244
In [47]:
Idents(brain.exp3.rna.v5) <- "brainregion"
In [48]:
brain.exp3.rna.new <- subset(brain.exp3.rna.v5, idents = c("mESC"), invert = TRUE)
In [40]:
table(brain.exp3.rna.new@meta.data$brainregion,brain.exp3.rna.new@meta.data$modality)
H3K27ac H3K27me3
AMY 2312 9119
ERC 3194 11305
NAC 1131 2502
PFC 927 5423
VTA_SnR 3582 5641
In [49]:
brain.exp3.rna.v5 <- brain.exp3.rna.new
In [50]:
rm(brain.exp3.rna.new)
In [ ]:
saveRDS(brain.exp3.rna, file = "03.MouseBrainExp3/merge_mtx/brain.exp3.rna.object_new.rds")
In [51]:
Idents(brain.exp5.rna.v5) <- "brainregion"
In [52]:
brain.exp5.rna.new <- subset(brain.exp5.rna.v5, idents = c("ITGremove"), invert = TRUE)
In [53]:
table(brain.exp5.rna.new@meta.data$brainregion,brain.exp5.rna.new@meta.data$modality)
H3K27ac H3K27me3
AMY 5117 7680
CPU 12033 21265
ERC 8944 12224
HCa 7772 15352
HCp 9541 13395
HYP 9109 14363
NAC 6048 12099
VTA_SnR 3952 5140
In [54]:
brain.exp5.rna.v5 <- brain.exp5.rna.new
In [55]:
rm(brain.exp5.rna.new)
In [ ]:
saveRDS(brain.exp5.rna, file = "05.MouseBrainExp5/merge_mtx/brain.exp5.rna.object_new.rds")
In [56]:
Idents(brain.exp13.rna.v5) <- "brainregion"
In [58]:
brain.exp13.rna.new <- subset(brain.exp13.rna.v5, idents = c("remove"), invert = TRUE)
In [59]:
table(brain.exp13.rna.new@meta.data$brainregion,brain.exp13.rna.new@meta.data$modality)
H3K27ac H3K27me3
HCa 10885 21233
HCp 14829 0
PFC 11698 43702
VTA_SnR 9413 7883
In [60]:
brain.exp13.rna.v5 <- brain.exp13.rna.new
In [61]:
rm(brain.exp13.rna.new)
In [ ]:
saveRDS(brain.exp13.rna, file = "13.MouseBrainExp13/merge_mtx/brain.exp13.rna.object_new.rds")
In [62]:
rm(bc,modality,replicate)
In [63]:
brain.exp1.rna.v5[["exp"]] <- "Exp1"
brain.exp2.rna.v5[["exp"]] <- "Exp2"
brain.exp3.rna.v5[["exp"]] <- "Exp3"
brain.exp4.rna.v5[["exp"]] <- "Exp4"
brain.exp5.rna.v5[["exp"]] <- "Exp5"
brain.exp6.rna.v5[["exp"]] <- "Exp6"
brain.exp7.rna.v5[["exp"]] <- "Exp7"
brain.exp8.rna.v5[["exp"]] <- "Exp8"
brain.exp9.rna.v5[["exp"]] <- "Exp9"
brain.exp10.rna.v5[["exp"]] <- "Exp10"
brain.exp11.rna.v5[["exp"]] <- "Exp11"
brain.exp12.rna.v5[["exp"]] <- "Exp12"
brain.exp13.rna.v5[["exp"]] <- "Exp13"
brain.exp14.rna.v5[["exp"]] <- "Exp14"
In [65]:
getwd()
'/projects/ps-renlab2/zhw063/99.MouseBrainPairedTag'
In [66]:
saveRDS(brain.exp1.rna.v5, file = "./seurat_v5_objects/brain.exp1.rna.object.rds")
saveRDS(brain.exp2.rna.v5, file = "./seurat_v5_objects/brain.exp2.rna.object.rds")
saveRDS(brain.exp3.rna.v5, file = "./seurat_v5_objects/brain.exp3.rna.object.rds")
saveRDS(brain.exp4.rna.v5, file = "./seurat_v5_objects/brain.exp4.rna.object.rds")
saveRDS(brain.exp5.rna.v5, file = "./seurat_v5_objects/brain.exp5.rna.object.rds")
saveRDS(brain.exp6.rna.v5, file = "./seurat_v5_objects/brain.exp6.rna.object.rds")
saveRDS(brain.exp7.rna.v5, file = "./seurat_v5_objects/brain.exp7.rna.object.rds")
saveRDS(brain.exp8.rna.v5, file = "./seurat_v5_objects/brain.exp8.rna.object.rds")
saveRDS(brain.exp9.rna.v5, file = "./seurat_v5_objects/brain.exp9.rna.object.rds")
saveRDS(brain.exp10.rna.v5, file = "./seurat_v5_objects/brain.exp10.rna.object.rds")
saveRDS(brain.exp11.rna.v5, file = "./seurat_v5_objects/brain.exp11.rna.object.rds")
saveRDS(brain.exp12.rna.v5, file = "./seurat_v5_objects/brain.exp12.rna.object.rds")
saveRDS(brain.exp13.rna.v5, file = "./seurat_v5_objects/brain.exp13.rna.object.rds")
saveRDS(brain.exp14.rna.v5, file = "./seurat_v5_objects/brain.exp14.rna.object.rds")
In [67]:
brain <- merge(brain.exp1.rna.v5, y=c(brain.exp2.rna.v5, brain.exp3.rna.v5, brain.exp4.rna.v5, brain.exp5.rna.v5,
brain.exp6.rna.v5, brain.exp7.rna.v5, brain.exp8.rna.v5, brain.exp9.rna.v5,
brain.exp10.rna.v5, brain.exp11.rna.v5, brain.exp12.rna.v5, brain.exp13.rna.v5, brain.exp14.rna.v5), project = "brain")
Error in eval(expr, envir, enclos): vector::reserve Traceback: 1. merge(brain.exp1.rna.v5, y = c(brain.exp2.rna.v5, brain.exp3.rna.v5, . brain.exp4.rna.v5, brain.exp5.rna.v5, brain.exp6.rna.v5, . brain.exp7.rna.v5, brain.exp8.rna.v5, brain.exp9.rna.v5, . brain.exp10.rna.v5, brain.exp11.rna.v5, brain.exp12.rna.v5, . brain.exp13.rna.v5, brain.exp14.rna.v5), project = "brain") 2. merge(brain.exp1.rna.v5, y = c(brain.exp2.rna.v5, brain.exp3.rna.v5, . brain.exp4.rna.v5, brain.exp5.rna.v5, brain.exp6.rna.v5, . brain.exp7.rna.v5, brain.exp8.rna.v5, brain.exp9.rna.v5, . brain.exp10.rna.v5, brain.exp11.rna.v5, brain.exp12.rna.v5, . brain.exp13.rna.v5, brain.exp14.rna.v5), project = "brain") 3. merge.Seurat(brain.exp1.rna.v5, y = c(brain.exp2.rna.v5, brain.exp3.rna.v5, . brain.exp4.rna.v5, brain.exp5.rna.v5, brain.exp6.rna.v5, . brain.exp7.rna.v5, brain.exp8.rna.v5, brain.exp9.rna.v5, . brain.exp10.rna.v5, brain.exp11.rna.v5, brain.exp12.rna.v5, . brain.exp13.rna.v5, brain.exp14.rna.v5), project = "brain") 4. merge(x = objects[[idx.x]][[assay]], y = lapply(X = objects[idx.y], . FUN = "[[", assay), labels = projects, add.cell.ids = NULL, . collapse = collapse, merge.data = merge.data) 5. merge.Assay(x = objects[[idx.x]][[assay]], y = lapply(X = objects[idx.y], . FUN = "[[", assay), labels = projects, add.cell.ids = NULL, . collapse = collapse, merge.data = merge.data) 6. RowMergeSparseMatrices(mat1 = counts.mats[[1]], mat2 = counts.mats[2:length(x = counts.mats)]) 7. RowMergeMatricesList(mat_list = all.mat, mat_rownames = all.rownames, . all_rownames = all.names)
In [68]:
library(scCustomize)
object_list <- list(brain.exp1.rna.v5,brain.exp2.rna.v5, brain.exp3.rna.v5, brain.exp4.rna.v5, brain.exp5.rna.v5,
brain.exp6.rna.v5, brain.exp7.rna.v5, brain.exp8.rna.v5, brain.exp9.rna.v5, brain.exp10.rna.v5,
brain.exp11.rna.v5, brain.exp12.rna.v5, brain.exp13.rna.v5, brain.exp14.rna.v5)
brain <- Merge_Seurat_List(list_seurat = object_list)
scCustomize v2.0.0 If you find the scCustomize useful please cite. See 'samuel-marsh.github.io/scCustomize/articles/FAQ.html' for citation info.
Error in eval(expr, envir, enclos): std::bad_alloc
Traceback:
1. Merge_Seurat_List(list_seurat = object_list)
2. reduce(list_seurat, function(x, y) {
. merge(x = x, y = y, merge.data = merge.data, project = project)
. })
3. reduce_impl(.x, .f, ..., .init = .init, .dir = .dir)
4. fn(out, elt, ...)
5. merge(x = x, y = y, merge.data = merge.data, project = project)
6. merge.Seurat(x = x, y = y, merge.data = merge.data, project = project)
7. merge(x = objects[[idx.x]][[assay]], y = lapply(X = objects[idx.y],
. FUN = "[[", assay), labels = projects, add.cell.ids = NULL,
. collapse = collapse, merge.data = merge.data)
8. merge.Assay(x = objects[[idx.x]][[assay]], y = lapply(X = objects[idx.y],
. FUN = "[[", assay), labels = projects, add.cell.ids = NULL,
. collapse = collapse, merge.data = merge.data)
9. RowMergeSparseMatrices(mat1 = data.mats[[1]], mat2 = data.mats[2:length(x = data.mats)])
10. RowMergeMatricesList(mat_list = all.mat, mat_rownames = all.rownames,
. all_rownames = all.names)
install.packages("scCustomize")
In [58]:
ls()
- 'brain.exp1.rna'
- 'brain.exp10.rna'
- 'brain.exp11.rna'
- 'brain.exp12.rna'
- 'brain.exp13.rna'
- 'brain.exp14.rna'
- 'brain.exp2.rna'
- 'brain.exp3.rna'
- 'brain.exp4.rna'
- 'brain.exp5.rna'
- 'brain.exp6.rna'
- 'brain.exp7.rna'
- 'brain.exp8.rna'
- 'brain.exp9.rna'